@article {837, title = {Image Segmentation via Stochastic Regional Texture Appearance Models}, journal = {Computer Vision and Image Understanding}, year = {2015}, abstract = {

An ongoing challenge in the area of image segmentation is in dealing with scenes exhibiting complex textural characteristics. While many approaches have been proposed to tackle this particular challenge, a related topic of interest that has not been fully explored for dealing with this challenge is stochastic texture models, particularly for characterizing textural characteristics within regions of varying sizes and shapes. Therefore, this paper presents a novel method for image segmentation based on the concept of multi-scale stochastic regional texture appearance models. In the proposed method, a multi-scale representation of the image is constructed using an iterative bilateral scale space decomposition. Local texture features are then extracted via image patches and random projections to generate stochastic texture features. A texton dictionary is built from the stochastic features, and used to represent the global texture appearance model. Based on this global texture appearance model, a regional texture appearance model can then be obtained based on the texton occurrence probability given a region within an image. Finally, a stochastic region merging algorithm that allows the computation of complex features is presented to perform image segmentation based on proposed regional texture appearance model. Experimental results using the BDSD300 segmentation dataset showed that the proposed method achieves a Probabilistic Rand Index (PRI) of 0.83 and an F-measure of 0.77@(0.92, 0.68), and provides improved handling of color and luminance variation, as well as strong segmentation performance for images with highly textured regions when compared to a number of previous methods. These results suggest that the proposed stochastic regional texture appearance model is better suited for handling the texture variations of natural scenes, leading to more accurate segmentations, particularly in situations characterized by complex textural characteristics.

}, keywords = {image segmentation, Regional texture appearance models, Stochastic texture models, texture segmentation}, doi = {http://dx.doi.org/10.1016/j.cviu.2015.06.001}, author = {R Sachett Medeiros and J Scharcanski and A Wong} } @article {139, title = {Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {50}, year = {2012}, pages = {1302 - 1317}, abstract = {

A region-based unsupervised segmentation and classification algorithm for polarimetric SAR imagery that incorporates region growing and a Markov random field (MRF) edge strength model is designed and implemented. This algorithm is an extension of the successful Iterative Region Growing with Semantics (IRGS) segmentation and classification algorithm, which was designed for amplitude only SAR imagery, to polarimetric data. Polarimetric IRGS (PolarIRGS) extends IRGS by incorporating a polarimetric feature model based on the Wishart distribution and modifying key steps such as initialization, edge strength computation and the region growing criterion. Like IRGS, PolarIRGS oversegments an image into regions and employs iterative region growing to reduce the size of the solution search space. The incorporation of an edge penalty in the spatial context model improves segmentation performance by preserving segment boundaries that traditional spatial models will smooth over. Evaluation of PolarIRGS with Flevoland fully polarimetric data shows that it improves upon two other recently published techniques in terms of classification accuracy.\ 

}, keywords = {complex, image segmentation, Markov random field (MRF), polarimetry, region adjacency graph (RAG), region-based, synthetic aperture radar (SAR), Wishart}, author = {P Yu and D A. Clausi and K Qin} } @article {170, title = {Decoupled active contour (DAC) for boundary detection}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {33}, year = {2011}, pages = {310 - 324}, abstract = {

The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.

}, keywords = {Computer Vision, decoupled active contour, Hidden Markov Model, hidden Markov models, image measurement, image segmentation, object boundary detection, object detection, search problems, segmentation accuracy, Viterbi optimizer, Viterbi search}, issn = {0162-8828}, doi = {http://dx.doi.org/10.1109/TPAMI.2010.83}, author = {A Mishra and P Fieguth and D A. Clausi} } @article {171, title = {Decoupled active contour (DAC) for boundary detection}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {33}, year = {2011}, pages = {310 - 324}, abstract = {

The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.

}, keywords = {Computer Vision, decoupled active contour, Hidden Markov Model, hidden Markov models, image measurement, image segmentation, object boundary detection, object detection, search problems, segmentation accuracy, Viterbi optimizer, Viterbi search}, issn = {0162-8828}, doi = {http://dx.doi.org/10.1109/TPAMI.2010.83}, author = {A Mishra and P Fieguth and D A. Clausi} } @article {179, title = {Synthesis of remote sensing label fields using a tree-structured hierarchical model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {49}, year = {2011}, pages = {2060-2070}, abstract = {

The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

}, keywords = {complex nonstationary scale structures, geographic information systems, geophysical image processing, image classification, image segmentation, Markov processes, operational RADARSAT SAR sea ice data, pseudoground truth data, region-oriented binary tree structure, remote sensing by radar, remote sensing label fields, resolution-oriented hierarchical method, SAR data analysis tools, sea ice, SIR-C/X-SAR land mass data, synthetic aperture radar, tree data structures, tree-structured hierarchical model}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2010.2102765}, author = {Y Liu and A Wong and P Fieguth} } @inproceedings {238, title = {Automated classification of operational SAR sea ice images}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, month = {06/2010}, address = {Ottawa, Ontario, Canada}, abstract = {

The automated classification of operational sea ice satellite imagery is important for ship navigation and environmental monitoring. Annually, thousands of large synthetic aperture radar (SAR) scenes are manually processed by the Canadian Ice Service (CIS) and pixel-level interpretation is not feasible. Trained ice analysts divide SAR images into $\#$x201D;polygon $\#$x201D; areas and then identify the number and type of ice classes per polygon. Full scene unsupervised classification can be performed by first segmenting each polygon into distinct regions algorithmically. Since there is insufficient information to assign a sea ice label for each region within an individual polygon, a Markov random field formulation using joint information to label each region in a full SAR scene has been developed. This approach has been successfully applied to operational CIS data to produce pixel-level classified images and is the first known successful end-to-end process for automatically classifying operational SAR sea ice images.

}, keywords = {automated classification, Canadian Ice Service, environmental monitoring, geophysical image processing, image segmentation, Markov processes, Markov random field, navigation, operational SAR sea ice images, pattern classification, pixel level interpretation, sea ice, sea ice satellite imagery, ship navigation, synthetic aperture radar, synthetic aperture radar scenes, unsupervised classification}, doi = {http://dx.doi.org/10.1109/CRV.2010.59}, author = {S Ochilov and D A. Clausi} } @inproceedings {245, title = {A cellular automata based semi-automatic algorithm for segmentation of choroidal blood vessels from ultrahigh resolution optical coherence images of rat retina}, booktitle = {23rd Canadian Conference on Electrical and Computer Engineering (CCECE)}, year = {2010}, month = {06/2010}, address = {Calgary, Alberta, Canada}, abstract = {

Abnormal changes in choroidal blood flow have been linked to various retinal diseases, such as Diabetic Retinopathy (DR) and Age related Macular Degeneration (AMD), which at later stages can lead to blindness. Therefore non-invasive and precise evaluation of choroidal blood flow can aid the diagnosis, treatment and monitoring of retinal disease progression. Doppler Optical Coherence Tomography is an imaging technique capable of measuring blood flow velocity and visualization of retinal and choroidal blood vessles. However accurate assessment of retinal and choroidal blood flow requires precise measurement of the blood vessel thickness. The presence of speckle noise and low image contrast of OCT tomograms makes this task very challenging. This paper proposes a cellular automata based semi-automatic algorithm for the segmentation of choroidal blood vessels. The proposed approach propagates user-defined points in order to identify the vessel boundaries, allowing a thickness profile to be extracted. The performance of the algorithm was tested on a series of retinal images acquired from living rats with a high speed, ultrahigh resolution OCT system (UHROCT). Experimental results show that the proposed approach provides precise thickness profiles even in the suboptimal conditions of low image contrast in the UHROCT images.
Abnormal changes in choroidal blood flow have been linked to various retinal diseases, such as Diabetic Retinopathy (DR) and Age related Macular Degeneration (AMD), which at later stages can lead to blindness. Therefore non-invasive and precise evaluation of choroidal blood flow can aid the diagnosis, treatment and monitoring of retinal disease progression. Doppler Optical Coherence Tomography is an imaging technique capable of measuring blood flow velocity and visualization of retinal and choroidal blood vessles. However accurate assessment of retinal and choroidal blood flow requires precise measurement of the blood vessel thickness. The presence of speckle noise and low image contrast of OCT tomograms makes this task very challenging. This paper proposes a cellular automata based semi-automatic algorithm for the segmentation of choroidal blood vessels. The proposed approach propagates user-defined points in order to identify the vessel boundaries, allowing a thickness profile to be extracted. The performance of the algorithm was tested on a series of retinal images acquired from living rats with a high speed, ultrahigh resolution OCT system (UHROCT). Experimental results show that the proposed approach provides precise thickness profiles even in the suboptimal conditions of low image contrast in the UHROCT images.
}, keywords = {age related macular degeneration, cellular automata, choroidal blood flow, choroidal blood vessel segmentation, diabetic retinopathy, Doppler optical coherence tomography, Feature Extraction, image resolution, image segmentation, medical image processing, OCT tomograms, optical tomography, rat retina image, retinal blood flow, retinal disease progression, thickness profile extraction, ultrahigh resolution optical coherence image}, doi = {http://dx.doi.org/10.1109/CCECE.2010.5575182}, author = {A Mishra and S Hariri and A Moayed and K Bizheva and P Fieguth and D A. Clausi} } @inproceedings {241, title = {Decoupled active surface for volumetric image segmentation}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, month = {06/2010}, address = {Ottawa, Ontario, Canada}, abstract = {

Finding the surface of a volumetric 3D object is a fundamental problem in computer vision. Energy minimizing splines, such as active surfaces, have been used to carry out such tasks, evolving under the influence of internal and external energies until the model converges to a desired surface. The present deformable model based surface extraction techniques are computationally expensive and are generally unreliable in identifying the surfaces of noisy, high-curvature and cluttered 3D objects. This paper proposes a novel decoupled active surface (DAS) for identifying the surface of volumetric 3D objects. The proposed DAS introduces two novel aspects which leads to robust, efficient and accurate convergence. First, rather than a parameterized surface, which leads to difficulties with complex shapes and parameter singularities, the DAS uses a conforming triangular mesh to represent the surface. Second, motivated by earlier successes in two-dimensional segmentation, the DAS treats the two energy components separately and uses novel solution techniques to efficiently minimize the two energy terms separately. The performance of DAS in segmenting static 3D objects is presented using several natural and synthetic volumetric images, with excellent convergence results.
}, keywords = {2D segmentation, Computer Vision, decoupled active surface, deformable model, energy minimizing splines, external energy, Feature Extraction, image segmentation, internal energy, mesh generation, splines (mathematics), surface extraction techniques, surface fitting, triangular mesh, volumetric 3D object, volumetric image segmentation}, doi = {http://dx.doi.org/10.1109/CRV.2010.45}, author = {A Mishra and P Fieguth and D A. Clausi} } @article {233, title = {Enabling scalable spectral clustering for image segmentation}, journal = {Pattern Recognition}, volume = {43}, year = {2010}, pages = {4069 - 4076}, abstract = {

Spectral clustering has become an increasingly adopted tool and an active area of research in the machine learning community over the last decade. A common challenge with image segmentation methods based on spectral clustering is scalability, since the computation can become intractable for large images. Down-sizing the image, however, will cause a loss of finer details and can lead to less accurate segmentation results. A combination of blockwise processing and stochastic ensemble consensus are used to address this challenge. Experimental results indicate that this approach can preserve details with higher accuracy than comparable spectral clustering image segmentation methods and without significant computational demands.

}, keywords = {image segmentation, Spectral clustering, Stochastic ensemble consesus}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/j.patcog.2010.06.015}, url = {http://dx.doi.org/10.1016/j.patcog.2010.06.015}, author = {F Tung and A Wong and D A. Clausi} } @article {234, title = {Interactive modeling and evaluation of tumor growth}, journal = {Journal of Digital Imaging}, volume = {23}, year = {2010}, pages = {755 - 768}, abstract = {

This paper addresses the need to quantify tumor growth and detect changes as this information is relevant to manage the patient treatment and to aid biotechnological efforts to cure cancer (Silva et al. 2008). An interactive tumor segmentation technique is used to recover the shape and size of tumors without imposing shape constraints. This segmentation algorithm provides good convergence, is robust to the initialization conditions, and requires simple and intuitive user interactions. A parametric approach to model tumor growth analytically is proposed in this paper. The preliminary experimental results are encouraging. The segmentation method is shown to be robust and simple to use, even in situations where the tumor boundary definition is challenging. Also, the experiments indicate that the proposed model potentially can be used to extrapolate the available data and help predict the tumor size (assuming unconstrained growth). Additionally, the proposed method potentially can provide a quantitative reference to compare the tumor shrinkage rate in cancer treatments.

}, keywords = {Active contours, cancer detection, chest radiographs, Computed tomography (CT), computer analysis, Computer Vision, computer-assisted diagnosis, Diagnostic Imaging, digital image processing, image segmentation, lung cancer}, issn = {0897-1889}, url = {http://dx.doi.org/10.1007/s10278-009-9234-4}, author = {D Koff and J Scharcanski and L da Silva and A Wong} } @article {272, title = {Intervertebral disc segmentation and volumetric reconstruction from peripheral quantitative computed tomography imaging}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {56}, year = {2010}, pages = {2748 - 2751}, abstract = {

An automatic system for segmenting and constructing volumetric representations of excised intervertebral discs from peripheral quantitative computed tomography (PQCT) imagery is presented. The system is designed to allow for automatic quantitative analysis of progressive herniation damage to the intervertebral discs under flexion/extension motions combined with a compressive load. Automatic segmentation and volumetric reconstruction of intervertebral disc from PQCT imagery is a very challenging problem due to factors such as streak artifacts and unclear material density separation between contrasted intervertebral disc and surrounding bone in the PQCT imagery, as well as the formation of multiple contrasted regions under axial scans. To address these factors, a novel multiscale level set approach based on the Mumford-Shaft energy functional in iterative bilateral scale space is employed to segment the intervertebral disc regions from the PQCT imagery. A Delaunay triangulation is then performed based on the set of points associated with the intervertebral disc regions to construct the volumetric representation of the intervertebral disc. Experimental results show that the proposed system achieves segmentation and volumetric reconstructions of intervertebral discs with mean absolute distance error below 0.8 mm when compared to ground truth measurements. The proposed system is currently in operational use as a visualization tool for studying progressive intervertebral disc damage.

}, keywords = {Animals, Artificial Intelligence, Automated, automatic quantitative analysis, Computer-Assisted, computerised tomography, Delaunay triangulation, flexion-extension motions, image reconstruction, image segmentation, Imaging, intervertebral disc segmentation, Intervertebral Disk, Intervertebral Disk Displacement, iterative bilateral scale space, material density separation, medical image processing, Mumford-Shah energy functional, Pattern Recognition, peripheral quantitative computed tomography imaging, progressive herniation damage, Radiographic Image Enhancement, Radiographic Image Interpretation, Swine, Three-Dimensional, Tomography, volumetric reconstruction, X-Ray Computed}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2009.2027225}, author = {A Wong and A Mishra and J Yates and P Fieguth and D A. Clausi and J Callaghan} } @article {223, title = {KPAC: A kernel-based parametric active contour method for fast image segmentation}, journal = {IEEE Signal Processing Letters}, volume = {17}, year = {2010}, pages = {312 - 315}, abstract = {

Object boundary detection has been a topic of keen interest to the signal processing and pattern recognition community. A popular approach for object boundary detection is parametric active contours. Existing parametric active contour approaches often suffer from slower convergence rates, difficulty dealing with complex high curvature boundaries, and are prone to being trapped in local optima in the presence of noise and background clutter. To address these problems, this paper proposes a novel kernel-based active contour (KPAC) approach, which replaces the conventional internal energy term used in existing approaches by incorporating an adaptive kernel derived for the underlying image characteristics. Experimental results demonstrate that the KPAC approach achieves state-of-the-art performance when compared to two other state-of-the-art parametric active contour approaches.

}, keywords = {edge detection, image segmentation, kernel-based parametric active contour approach, KPAC approach, object boundary detection, Pattern Recognition}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2009.2036654}, author = {A Mishra and A Wong} } @article {225, title = {Multivariate image segmentation using semantic region growing with adaptive edge penalty}, journal = {IEEE Transactions on Image Processing}, volume = {19}, year = {2010}, pages = {2157 - 2170}, abstract = {

Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness and solution space complexity that impose computational cost and degrade algorithmic robustness. To deal with these problems, a Markov random field (MRF) based multivariate segmentation algorithm called $\#$x201C;multivariate iterative region growing using semantics $\#$x201D; (MIRGS) is presented. In MIRGS, the impact of intraclass variation and computational cost are reduced using the MRF spatial context model incorporated with adaptive edge penalty and applied to regions. Semantic region growing starting from watershed over-segmentation and performed alternatively with segmentation gradually reduces the solution space size, which improves segmentation effectiveness. As a multivariate iterative algorithm, MIRGS is highly sensitive to initial conditions. To suppress initialization sensitivity, it employs a region-level k -means (RKM) based initialization method, which consistently provides accurate initial conditions at low computational cost. Experiments show the superiority of RKM relative to two commonly used initialization methods. Segmentation tests on a variety of synthetic and natural multivariate images demonstrate that MIRGS consistently outperforms three other published algorithms.

}, keywords = {adaptive edge penalty, Algorithms, Automated, Computer-Assisted, image enhancement, image interpretation, image segmentation, iterative methods, Markov processes, Markov random field, MRF spatial context model, Multivariate Analysis, multivariate image segmentation, multivariate iterative region growing, Pattern Recognition, region-level k -means based initialization, Reproducibility of Results, semantic region growing, Semantics, Sensitivity and Specificity}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2010.2045708}, author = {K Qin and D A. Clausi} } @inproceedings {287, title = {IceSynth: An image synthesis system for sea-ice segmentation evaluation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.

}, keywords = {automatic sea-ice monitoring, ice classification, ice types, IceSynth, image classification, image segmentation, image synthesis system, image texture, oceanographic techniques, polar regions, probability, probability distribution estimation, pseudo-ground truth data, remote sensing by radar, SAR imagery, sea ice, sea-ice segmentation evaluation, sea-ice structures, sea-ice textures, stochastic processes, stochastic sampling, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2009.27}, author = {A Wong and W Zhang and D A. Clausi} } @inproceedings {289, title = {A novel algorithm for extraction of the layers of the cornea}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

Accurate corneal layer boundary extraction from optical coherence tomograms can provide precise layer thickness measurements required in the analysis of corneal disease. This paper establishes a novel approach to precisely obtain the five primary corneal layer boundaries. The proposed method determines correspondence relationships between the layer boundaries to facilitate robust boundary extraction in the presence of noise and artifacts. The first phase of the method applies morphological operators to enhance the prominent structural features of the cornea. The second phase uses a semi-automated segmentation algorithm to extract the upper and lower boundaries of the cornea; these boundaries are used to register the corneal image. The final phase extracts all five boundaries using a global optimization method exploiting the medial correspondence relationship between each layers. The proposed method is tested and verified using a representative set of optical coherence tomography images and compared against several state of the art methods. The proposed method is demonstrated to be more robust to noise, to provide more accurate segmentation results, and to require fewer user interactions than the other published methods.

}, keywords = {corneal disease, corneal image register, corneal layer boundary extraction, diseases, eye, Feature Extraction, global optimization method, image registration, image segmentation, layer thickness measurements, medical image processing, optical coherence tomography, optical tomography, optimisation, semiautomated segmentation algorithm, thickness measurement}, doi = {http://dx.doi.org/10.1109/CRV.2009.22}, author = {J A. Eichel and A Mishra and D A. Clausi and P Fieguth} } @inproceedings {283, title = {SAR sea ice image segmentation using an edge-preserving region-based MRF}, booktitle = {16th IEEE International Conference on Image Processing (ICIP)}, year = {2009}, month = {11/2009}, abstract = {

In this paper, we propose a novel edge-preserving region (EPR)-based representation for synthetic aperture radar (SAR) images, which is incorporated with a region-level Markov random field (MRF) model to offer an efficient approach to the segmentation of SAR sea ice images. The EPR-based representations of SAR images are constructed by applying the speckle reduction anisotropic diffusion (SRAD) algorithm and the watershed transform, which aims at suppressing oversegmentation within objects while accurately locating object edges at region boundaries in the presence of speckle noise. In combination with a region-level MRF, the EPR-based representation largely reduces the search space of optimization process and improves parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. Relative to the existing region-level MRF-based methods, testing results have demonstrated that the proposed method achieves more than 50\% reduction of computational time and improves the segmentation accuracy especially at high speckle noise.

}, keywords = {edge-preserving region, image segmentation, Markov processes, MRF model, parameter estimation, region-level Markov random field, SAR sea ice image segmentation, sea ice, speckle noise, speckle reduction anisotropic diffusion, SRAD algorithm, synthetic aperture radar, synthetic aperture radar images, watershed transform}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5414606}, author = {X Yang and D A. Clausi} } @inproceedings {290, title = {SEC: Stochastic ensemble consensus approach to unsupervised SAR sea-ice segmentation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the underlying ice type, which is a challenging task to perform automatically due to various imaging and environmental conditions. A novel stochastic ensemble consensus approach to sea-ice segmentation (SEC) is presented to tackle this challenging task. In SEC, each pixel in the SAR sea-ice image is assigned an initial sub-class based on its tonal characteristics. Ensembles of random samples are generated from a random field representing the SAR sea-ice imagery. The generated ensembles are then used to re-estimate the sub-class of the pixels using a weighted median consensus strategy. Based on the probability distribution of the sub-classes, an expectation maximization (EM) approach is utilized to estimate the final class likelihoods using a Gaussian mixture model (GMM). Finally, maximum likelihood (ML) classification is performed to estimate the final class of each pixel within the SAR sea-ice imagery based on the estimated GMM and the assigned sub-classes. SEC was tested using a variety of operational RADARSAT-1 and RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) and was shown to produce successfully segmentation results that were superior to approaches based on K-means clustering, Gamma mixture models, and Markov Random Field (MRF) models for sea-ice segmentation.

}, keywords = {Canadian Ice Service, expectation maximization approach, Gaussian mixture model, GMM, image segmentation, K-means clustering, Markov Random Field models, maximum likelihood classification, maximum likelihood estimation, oceanographic techniques, polar regions, probability, probability distribution, RADARSAT-1 imagery, RADARSAT-2 imagery, random samples, remote sensing by radar, SAR sea-ice imagery, sea ice, sea-ice analysis, sea-ice monitoring, stochastic ensemble consensus approach, synthetic aperture radar, tonal characteristics, underlying ice type, unsupervised SAR sea-ice segmentation, weighted median consensus strategy}, doi = {http://dx.doi.org/10.1109/CRV.2009.25}, author = {A Wong and D A. Clausi and P Fieguth} } @inproceedings {332, title = {Background estimation for microscopic cellular images}, booktitle = {15th IEEE International Conference on Image Processing, 2008}, year = {2008}, abstract = {

Background modelling is a key task in tracking applications. Our interest in this paper is the accurate estimation of static backgrounds in scientific imaging, such as those in automated stem cell tracking. In this paper, an effective background estimation method is proposed. First, the segmentation results are used to remove the foreground objects, then the background is robustly estimated over the resultant 3-Dimensional residual image sequence. We do spatio-temporal background estimation over a local neighbourhood with a robust trimmed mean. The experimental results generated by the proposed method are quite promising.

}, keywords = {automated stem cell tracking, Biomedical Imaging, cellular biophysics, foreground objects removal, image segmentation, image sequence, image sequences, medical image processing, microscopic cellular images, Scientific Imaging, spatio-temporal background estimation, static backgrounds, tracking applications}, doi = {http://dx.doi.org/10.1109/ICIP.2008.4712436}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {333, title = {Deblocking of block-transform compressed images using phase-adaptive shifted thresholding}, booktitle = {IEEE International Symposium on Multimedia}, year = {2008}, abstract = {

Many popular image compression schemes are based on block-transform coding, a technique where images are broken into small blocks of pixels prior to transformation and compression. Block-transform coding often introduces blocking artifacts which are particularly prevalent at low bit-rates due to quantization errors. A novel algorithm for deblocking block-transform compressed images is proposed in this paper. This algorithm is based on a phase-adaptive, shifted thresholding technique that estimates the original uncompressed image as the weighted sum of shifted versions of the decompressed image subjected to a threshold. An efficient integer transform is used to construct the shifted versions of the decompressed image. The aggregation weights are obtained adaptively using the local phase moment characteristics of the underlying image content. The proposed algorithm utilizes important human perceptual characteristics to provide effective image deblocking while preserving image detail. Experimental results show that the proposed algorithm is more efficient than comparable methods and yields both subjective results and peak signal-to-noise ratio (PSNR) results comparable to existing methods.

}, keywords = {aggregation weight, block-transform coding, block-transform image compression deblocking, data compression, human perceptual characteristics, image coding, image segmentation, integer transform, local phase moment, phase-adaptive shifted thresholding, quantisation (signal), quantization error, transforms}, doi = {http://dx.doi.org/10.1109/ISM.2008.74}, author = {A Wong and W Bishop} } @article {316, title = {IRGS: Image segmentation using edge penalties and region growing}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {30}, year = {2008}, pages = {2126 - 2139}, abstract = {

This paper proposes an image segmentation method named iterative region growing using semantics (IRGS), which is characterized by two aspects. First, it uses graduated increased edge penalty (GIEP) functions within the traditional Markov random field (MRF) context model in formulating the objective functions. Second, IRGS uses a region growing technique in searching for the solutions to these objective functions. The proposed IRGS is an improvement over traditional MRF based approaches in that the edge strength information is utilized and a more stable estimation of model parameters is achieved. Moreover, the IRGS method provides the possibility of building a hierarchical representation of the image content, and allows various region features and even domain knowledge to be incorporated in the segmentation process. The algorithm has been successfully tested on several artificial images and synthetic aperture radar (SAR) images.

}, keywords = {Algorithms, artificial images, Artificial Intelligence, Automated, Computer-Assisted, edge strength information, GIEP, graduated increased edge penalty, image enhancement, image interpretation, image segmentation, IRGS, iterative methods, iterative region growing, Markov processes, Markov random field context model, model parameter estimation, MRF, Pattern Recognition, random processes, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, synthetic aperture radar images}, doi = {10.1109/TPAMI.2008.15}, author = {Q Yu and D A. Clausi} } @inproceedings {366, title = {A medical texture local binary pattern for TRUS prostate segmentation}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.

}, keywords = {automated segmentation systems, biomedical ultrasonics, cancer, digital imaging techniques, image classification, image segmentation, image texture, LBP, level set contour deformations, medical image processing, medical texture local binary pattern, prostate cancer diagnosis, prostate cancer treatment, short gray level range, speckle noise, texture analysis, Texture classification, texture segmentation, transrectal ultrasound prostate image segmentation, TRUS prostate segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353617}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {362, title = {SAR sea ice image segmentation based on edge-preserving watersheds}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

This paper presents a hybrid method for the segmentation of SAR sea ice images, which consists of an initial watershed segmentation followed by a region merging. Iterative bilateral filtering is used to reduce speckle noise and suppress irrelevant image details, which can significantly alleviate oversegmentation of watersheds. Since edges are well preserved by bilateral filtering, the watershed algorithm is capable of precisely locating object boundaries. Final segmentation is accomplished by applying an iterative region merging on the watershed regions by taking into account local boundary strengths and regional statistics. The efficiency of the proposed method has been demonstrated on the segmentation of SAR sea ice images. In comparison with traditional watershed algorithm, our method achieves better performance in identifying filament structures such as leads.

}, keywords = {edge-preserving watersheds, filtering theory, image segmentation, iterative bilateral filtering, iterative methods, local boundary strengths, radar imaging, region merging, regional statistics, SAR sea ice image segmentation, speckle noise, statistical analysis, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2007.58}, author = {X Yang and D A. Clausi} } @inproceedings {369, title = {Stem-cell localization: a deconvolution problem}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Hematopoietic Stem Cells (HSCs) form blood and immune cells and are responsible for the constant renewal of blood. To produce new blood cells, HSCs proliferate and differentiate to different blood cell types continuously during their lifetime. Hence they are of substantial interest in stem cell therapy and cancer research. To classify HSCs to different groups, they must be observed/tracked over time and their key features including cell size, shape, and motility must be extracted. The manual tracking is an onerous task and automated methods are in high demand. The first stage of an semi-automatic/automatic tracking system is cell segmentation. In our previous work we addressed the cell segmentation/localization problem. Modelling adjacent or splitting cells is very challenging and our previous methods might fail to accurately model a group of adjacent cells or a splitting cell. In this paper we address this issue and propose a deconvolution method to precisely model individual HSCs as well as adjacent (splitting) HSCs. An optimization algorithm is combined with a template matching method to segment cell regions and locate the cell centers.

}, keywords = {adjacent cells, biomedical optical imaging, blood, cell segmentation, cellular biophysics, deconvolution, deconvolution problem, hematopoietic stem cells, image segmentation, immune cells, medical image processing, optimisation, optimization algorithm, stem-cell localization, template matching method}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353597}, author = {N Nezamoddin Kachouie and P Fieguth and E Jervis} } @inproceedings {749, title = {An elliptical level set method for automatic TRUS prostate image segmentation}, booktitle = {IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

One of the most important tasks in prostate cancer diagnosis and treatment is segmentation of transrectal ultrasound (TRUS) prostate images. Due to the large volumes of TRUS prostate images, automatic segmentation systems are mandatory. Weak prostate boundaries, speckle noise and the short range of gray levels make the task more challenging and difficult. Deformable models have been considered as an effective approach for semi-automatic prostate segmentation. However the main problem toward a fully automatic segmentation system using deformable models is initialization of seed or control points. In this paper an automatic level set prostate segmentation is presented. A classification method is employed to locate the approximate location of the prostate which is used to initiate the proposed elliptical level set contour. The deformations of the level set are guided by a velocity function which is derived using the TRUS prostate image histogram

}, keywords = {automatic level set prostate segmentation, automatic TRUS prostate image segmentation, biomedical ultrasonics, cancer, classification method, elliptical level set method, image classification, image segmentation, medical image processing, prostate cancer diagnosis, speckle noise, transrectal ultrasound, velocity function, weak prostate boundaries}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270795}, author = {N Kachouie and N Nezamoddin and P Fieguth and S Rahnamayan} } @article {383, title = {Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {44}, year = {2006}, pages = {3687 - 3694}, abstract = {

Modeling spatial context constraints using a Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to synthetic aperture radar (SAR) sea-ice segmentation has also been demonstrated recently. However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and necessary for identifying certain ice types. In this paper, a new statistical context model is proposed that, within the same scene, can simultaneously preserve narrow elongated features while producing similar smooth segmentation results comparable to typical MRF-based approaches. Tested on one synthetic image and two SAR sea-ice scenes, this filament preserving model substantially improves classification accuracies when compared to standard Gaussian mixture and MRF-based segmentation algorithms

}, keywords = {egg code, filament preserving model, image classification, image segmentation, Markov processes, Markov random field, MRF-based segmentation algorithm, noisy image segmentation, oceanographic techniques, remote sensing by radar, SAR sea-ice imagery, sea ice, ship navigation, standard Gaussian mixture, synthetic aperture radar, synthetic image}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2006.885046}, author = {Q Yu and D A. Clausi} } @inproceedings {393, title = {Filament preserving segmentation for SAR sea ice imagery using a new statistical model}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Modelling spatial context constraints using Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to SAR sea ice segmentation has also been demonstrated by Deng and Clausi (2005). However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. A new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing MRF based approaches

}, keywords = {Feature Extraction, filament preserving segmentation, geophysical signal processing, Image Denoising, image segmentation, Markov processes, Markov random field, narrow elongated features, noisy image segmentation, oceanographic techniques, radar imaging, SAR sea ice imagery, sea ice, ship navigation, spatial context constraints, statistical analysis, statistical model, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/ICPR.2006.561}, author = {Q Yu and D A. Clausi} } @inproceedings {398, title = {Hierarchical region mean-based image segmentation}, booktitle = {3rd Canadian Conference on Computer and Robot Vision}, year = {2006}, abstract = {

Gibbs Random Fields (GRFs), which produce elegant models, but which have very poor computational speed have been widely applied to image segmentation. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a more natural model in segmenting real images. In this paper, we show that the fineto-coarse region-based hierarchical regions framework for the well-known Potts model can be extended to non-edge based interactions. By deliberately oversegmenting at the finer scale, the method proceeds conservatively by avoiding the construction of regions which straddle a region boundary by computing region mean differences. This demonstrates the hierarchical method is able to model region interactions through new generalizations at higher levels in the hierarchy which represent regions. Promising results are presented

}, keywords = {Gibbs Random Fields, hierarchical models, image segmentation, Markov Random Fields, region-based}, isbn = {0-7695-2542-3}, doi = {http://dx.doi.org/10.1109/CRV.2006.39}, author = {S Wesolkowski and P Fieguth} } @inproceedings {750, title = {A hybrid algorithm using discrete cosine transform and Gabor filter bank}, booktitle = {17th Canadian Conference on Electrical and Computer Engineering}, year = {2006}, abstract = {

Gabor filters have been widely used for texture segmentation and feature extraction, however there are important considerations regarding filter parameters, filter bank coverage in the frequency domain and feature dimensional reduction. In this paper, a texture segmentation algorithm based on a hybrid filter bank is presented. The proposed method uses a Gabor filter bank and discrete cosine transform (GDCT) to extract the optimal features for texture segmentation. To reduce the feature vector dimension a competitive network is trained to estimate the principal components of the extracted features. The feature vectors composing both Gabor and DCT features are quantized by estimated eigenvectors. The proposed method enables the use of multiple filter banks or larger filter banks consisting of a higher number of channels.

}, keywords = {channel bank filters, competitive network training, discrete cosine transform, discrete cosine transforms, eigenvalues and eigenfunctions, eigenvector quantization, feature dimensional reduction, Feature Extraction, filter bank coverage, filter parameters, Gabor filter bank, GDCT, hybrid algorithm, hybrid filter bank, image segmentation, image texture, multiple filter banks, neural nets, neural networks, optimal features, principal component analysis, principal components estimation, texture segmentation, unsupervised learning}, doi = {http://dx.doi.org/10.1109/CCECE.2004.1349767}, author = {N Nezamoddin Kachouie and J Alirezaie and P Fieguth} } @inproceedings {394, title = {Joint image segmentation and interpretation using iterative semantic region growing on SAR sea ice imagery}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. Typically, the segmentation and interpretation are performed separately with the interpretation as a post processing of segmentation. In this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. The segmentation algorithm is based on a region growing technique and the interpretation is a Markov random field (MRF) based classification. The two processes are integrated under the Bayesian framework, with both aiming at reducing a defined energy. The interactions between the two are bidirectional by letting the interpretation result have some degree of control on the region growing process. Various features can hence be efficiently combined, and accurate classifications are obtained for operational synthetic aperture radar (SAR) sea ice applications

}, keywords = {Bayes methods, Bayesian framework, image analysis, image classification, image interpretation, image segmentation, iterative methods, iterative semantic region growing, Markov processes, Markov random field based classification, radar imaging, SAR sea ice imagery, sea ice, semantic class labels, synthetic aperture radar, terrain mapping}, doi = {http://dx.doi.org/10.1109/ICPR.2006.734}, author = {Q Yu and D A. Clausi} } @inproceedings {408, title = {A statistical thresholding method for cell tracking}, booktitle = {2006 IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

Tracking the motion of cells in culture is a task, which often still Is undertaken manually, and for which automated methods are strongly desirable. Researchers visually perform cell motion analysis, observe cell movements and cell shape changes for hours to discover when, where and how fast It moves, splits or dies. Hematopoletlc stem cells (HSCs) proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. In this paper a statistical method is introduced to track HSCs over time. A statistical thresholding method is combined with joint probabilistic data association in the proposed HSC tracker

}, keywords = {blood, blood cell, cancer, cell motion analysis, cell tracking, cellular biophysics, gene therapy, hematopoletlc stem cells proliferate, image motion analysis, image segmentation, medical image processing, statistical analysis, statistical thresholding method, stem-cell research}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270801}, author = {J Ramunas and N Nezamoddin Kachouie and P Fieguth and E Jervis} } @inproceedings {423, title = {Adaboost and support vector machines for white matter lesion segmentation in MR images}, booktitle = {27th Annual International Conference of the Engineering in Medicine and Biology Society}, year = {2005}, abstract = {

The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided

}, keywords = {Adaboost, biomedical MRI, brain, human brain, image classification, image segmentation, medical image processing, MR images, proton density scans, radial basis function, radial basis function networks, support vector machines, T1 acquisition, white matter lesion segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2005.1616447}, author = {A Quddus and P Fieguth and O Basir} } @inproceedings {644, title = {Combining local and global features for image segmentation using iterative classification and region merging}, booktitle = {2nd Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, abstract = {

In MRF based unsupervised segmentation, the MRF model parameters are typically estimated globally. Those global statistics sometimes are far from accurate for local areas if the image is highly non-stationary, and hence will generate false boundaries. The problem cannot be solved if local statistics are not considered. This work incorporates the local feature of edge strength in the MRF energy function, and segmentation is obtained by reducing the energy function using iterative classification and region merging.

}, keywords = {edge detection, edge strength, Feature Extraction, global feature, global statistics, image classification, image segmentation, iterative classification, iterative methods, local feature, local statistics, Markov processes, Markov random field, MRF based unsupervised segmentation, MRF energy function, MRF model parameter, region merging}, author = {Q Yu and D A. Clausi} } @inproceedings {643, title = {Comparing classification metrics for labeling segmented remote sensing images}, booktitle = {2nd Annual Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, address = {Victoria, B.C., Canada}, abstract = {

Image segmentation and labelling are the two conceptual operations in image classification. As the remote sensing community uses more powerful segmentation procedures with spatial constraint, new possibilities can be explored for labelling. Instead of assigning a label to a single observation (pixel), whole segments of image are labelled at once implying the use of multivariate samples rather than pixel vectors. This approach to image classification also offers new possibilities for using a priori information about the classes such as existing maps or object signature libraries. The present paper addresses the two issues. First a labelling scheme is presented that gathers evidence about the classes from incomplete a priori information using a \"cognitive reasoning\" approach. Then, five different metrics are compared for the label assignment and are combined through a voting scheme. The results show that very different results can be obtained depending on the metric chosen. The metric combination through voting, being a suboptimal approach does not necessarily provide the best results but could be a safe alternative to choosing only one metric.

}, keywords = {a priori information, classification metrics, cognitive reasoning, image classification, image labelling, image segmentation, label assignment, multivariate samples, Remote Sensing, segmented remote sensing images, voting scheme}, doi = {http://dx.doi.org/10.1109/CRV.2005.28}, author = {P Maillard and D A. Clausi} } @article {415, title = {Design-based texture feature fusion using Gabor filters and co-occurrence probabilities}, volume = {14}, year = {2005}, pages = {925 - 936}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. The fused feature set utilizes both the Gabor filter\&$\#$39;s capability of accurately capturing lower and mid-frequency texture information and the GLCP\&$\#$39;s capability in texture information relevant to higher frequency components. Evaluation methods include comparing feature space separability and comparing image segmentation classification rates. The fused feature sets are demonstrated to produce higher feature space separations, as well as higher segmentation accuracies relative to the individual feature sets. Fused feature sets also outperform individual feature sets for noisy images, across different noise magnitudes. The curse of dimensionality is demonstrated not to affect segmentation using the proposed the 48-dimensional fused feature set. Gabor magnitude responses produce higher segmentation accuracies than linearly normalized Gabor magnitude responses. Feature reduction using principal component analysis is acceptable for maintaining the segmentation performance, but feature reduction using the feature contrast method dramatically reduced the segmentation accuracy. Overall, the designed fused feature set is advocated as a means for improving texture segmentation performance.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, design-based texture feature fusion, feature contrast, feature reduction, Gabor filter, grey level cooccurrence probability, image classification, image enhancement, image interpretation, image segmentation, image segmentation classification, image texture, Information Storage and Retrieval, Models, Pattern Recognition, principal component analysis, probability, Statistical, Subtraction Technique, texture recognition}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2005.849319}, author = {D A. Clausi and H Deng} } @article {417, title = {Operational map-guided classification of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {2940 - 2951}, abstract = {

This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual \"egg code\" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71\% and 89\%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.

}, keywords = {Beaufort Sea, Canadian Ice Service, Fisher criterion, geophysical signal processing, gray-level cooccurrence matrix, Gulf of Saint Lawrence, image classification, image segmentation, image texture, Kolmogorov-Smirnov test, Mahalanobis distance, map-guided classification, Markov processes, Markov random field, oceanographic regions, oceanographic techniques, pixel-based ice maps, radar imaging, remote sensing by laser beam, remote sensing by radar, SAR sea ice imagery, sea ice, sea ice classification, segment distribution comparison, synthetic aperture radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2005.857897}, author = {P Maillard and D A. Clausi and H Deng} } @inproceedings {422, title = {A probabilistic living cell segmentation model}, booktitle = {International Conference on Image Analysis and Recognition}, year = {2005}, abstract = {

A better understanding of cell behavior is very important in drug and disease research. Cell size, shape, and motility may play a key role in stem-cell specialization or cancer development. However the traditional method of inferring these values from image sequences manually is such an onerous task that automated methods of cell tracking and segmentation are in high demanded, especially given the increasing amount of cell data being collected. In this paper, a novel probabilistic cell model is designed to segment the individual hematopoietic stem cells (HSCs) extracted from mice bone marrow cells. The proposed cell model has been successfully applied to HSC segmentation, identifying the most probable cell locations in the image on the basis of cell brightness and morphology.

}, keywords = {bone marrow cells, cancer development, cellular biophysics, disease research, hematopoietic stem cells, image segmentation, image sequences, medical image processing, probabilistic living cell segmentation model, probability, stem-cell specialization}, doi = {http://dx.doi.org/10.1109/ICIP.2005.1529956}, author = {N Nezamoddin Kachouie and L J Lee and P Fieguth} } @article {438, title = {Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {42}, year = {2004}, pages = {215 - 228}, abstract = {

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.

}, keywords = {Brodatz, discrimination ability, Feature Extraction, GLCP, gray-level cooccurrence probabilities, image segmentation, image texture, Markov processes, Markov Random Fields, MRF, Remote Sensing, remote sensing by radar, SAR sea ice imagery, sea ice, synthetic aperture radar, texture analysis, texture boundary, texture feature consistency, texture feature separability, texture methods, window size}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2003.817218}, author = {D A. Clausi and B Yue} } @inproceedings {629, title = {Feature fusion for image texture segmentation}, booktitle = {17th International Conference on Pattern Recognition (ICPR)}, year = {2004}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. Feature space separability and unsupervised image segmentation are used for testing. The fused features are robust with respect to the curse of dimensionality and additive noise. Feature reduction methods are typically detrimental to the segmentation performance. Overall, the fused features are a definite improvement over non-fused features and are advocated in texture analysis applications.

}, keywords = {additive noise, design based method, Feature Extraction, feature fusion, feature reduction method, Gabor filter, grey level cooccurrence probability, image segmentation, image texture, image texture segmentation, probability, texture analysis, texture recognition, unsupervised image segmentation}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334207}, author = {D A. Clausi and H Deng} } @inproceedings {627, title = {Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields}, booktitle = {17th International Conference on Pattern Recognition (ICPR)}, year = {2004}, month = {08/2004}, address = {Cambridge, United Kingdom}, abstract = {

The discrimination ability of texture features derived from Gaussian Markov random fields (GMRFs) and grey level co-occurrence probabilities (GLCPs) are compared and contrasted. More specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, an important concept when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than GMRFs.

}, keywords = {discrimination ability, Feature Extraction, Gaussian Markov random fields, Gaussian processes, grey level co-occurrence probabilities, image segmentation, image texture, Markov processes, probability, texture features, texture segmentation}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334208}, author = {D A. Clausi and B Yue} } @article {621, title = {Unsupervised image segmentation using a simple MRF model with a new implementation scheme}, journal = {Pattern Recognition}, volume = {37}, year = {2004}, pages = {2323{\textendash}2335}, abstract = {

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture)and achieves an improvement over the traditional method.

}, keywords = {Color Image, Expectation-maximization (EM) algorithm, Feature space, image segmentation, K-means clustering, Markov random field (MRF), sea ice, synthetic aperture radar (SAR), Texture Image, unsupervised segmentation}, author = {H Deng and D A. Clausi} } @inproceedings {445, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using MRF models}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, month = {05/2004}, address = {London, Ontario, Canada}, abstract = {

Due to both environmental and sensor reasons, it is challenging to develop computer-assisted algorithms to segment SAR (synthetic aperture radar) sea ice imagery. In this research, images containing either ice and water or multiple ice classes are segmented. This paper proposes to use the image intensity to discriminate ice from water and to use texture features to separate different ice types. In order to seamlessly combine spatial relationship information in an ice image with various image features, a novel Bayesian segmentation approach is developed Experiments demonstrate that the proposed algorithm is able to segment both types of sea ice images and achieves an improvement over the standard MRF (Markov random field) based method, the finite Gamma mixture model and the K-means clustering method.

}, keywords = {expectation-maximization (EM), Gamma distribution, image feature, image segmentation, K-means clustering, Markov random field (MRF), mixture model, sea ice, synthetic aperture radar (SAR), texture, unsupervised segmentation}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301420}, author = {H Deng and D A. Clausi} } @inproceedings {577, title = {Color image segmentation using connected regions}, booktitle = {16th Canadian Conference on Electrical and Computer Engineering}, year = {2003}, abstract = {

A color image segmentation algorithm based on region growing is presented. Each region is characterized using two parameters: within region color contrast and between region color contrast. The first parameter is the distance between the two most distant pixels in terms of color. The second parameter is the distance between the candidate pixel and its nearest neighbor in the region. The color similarity measure used is the vector angle, which is invariant to shading. Highlight invariance is accomplished by using a highlight removal transformation, which removes the average pixel intensity from each RGB coordinate. The first calculation is very computationally intensive. To reduce this computational burden, the algorithm keeps track of which pixels already in the region are furthest spatially from the pixel being considered. The assumption would be that the pixels the furthest away would be the ones most different from the pixel being considered. We will present results on artificial and real images to illustrate the effectiveness of the method.

}, keywords = {color image segmentation, color similarity measure, connected region, highlight invariance, highlight removal transformation, image colour analysis, image segmentation, pixel intensity, real image, realistic images, RGB coordinate, shading invariance, vector angle}, doi = {http://dx.doi.org/10.1109/CCECE.2003.1226114}, author = {S Wesolkowski and P Fieguth} } @inproceedings {649, title = {Operational segmentation and classification of SAR sea ice imagery}, booktitle = {2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, An Honorary Workshop for Prof. David A. Landgrebe}, year = {2003}, abstract = {

The Canadian Ice Service (CIS) is a government agency responsible for monitoring ice-infested regions in Canada\&$\#$39;s jurisdiction. Synthetic aperture radar (SAR) is the primary tool used for monitoring such vast, inaccessible regions. Ice maps of different regions are generated each day in support of navigation operations and environmental assessments. Currently, operators digitally segment the SAR data manually using primarily tone and texture visual characteristics. Regions containing multiple ice types are identified, however, it is not feasible to produce a pixel-based segmentation due to time constraints. In this research, advanced methods for performing texture feature extraction, incorporating tonal features, and performing the segmentation are presented. Examples of the segmentation of a SAR image that is difficult to segment manually and that requires the inclusion of both tone and texture features are presented.

}, keywords = {Canada, Canadian Ice Service, environmental assessments, Feature Extraction, geophysical signal processing, government agency, ice maps, image classification, image segmentation, image texture, navigation operations, oceanographic regions, operational segmentation, pixel based segmentation, radar imaging, SAR image, SAR sea ice imagery, sea ice, synthetic aperture radar, texture visual characteristics, tone visual characteristics}, doi = {http://dx.doi.org/10.1109/WARSD.2003.1295204}, author = {D A. Clausi and H Deng} } @inproceedings {602, title = {A probabilistic framework for image segmentation}, booktitle = {IEEE International Conference on Image Processing}, year = {2003}, abstract = {

A new probabilistic image segmentation model based on hypothesis testing and Gibbs random fields is introduced. First, a probabilistic difference measure derived from a set of hypothesis tests is introduced. Next, a Gibbs/Markov random field model endowed with the new measure is then applied to the image segmentation problem to determine the segmented image directly through energy minimization. The Gibbs/Markov random fields approach permits us to construct a rigorous computational framework where local and regional constraints can be globally optimized. Results on grayscale and color images are encouraging.

}, keywords = {energy minimization, Gibbs random field, hypothesis testing, image segmentation, Markov random field, probabilistic image segmentation, probability}, doi = {http://dx.doi.org/10.1109/ICIP.2003.1246714}, author = {S Wesolkowski and P Fieguth} } @inproceedings {591, title = {Adaptive color image segmentation using Markov random fields}, booktitle = {International Conference on Pattern Recognition}, year = {2002}, address = {Rochester, NY}, abstract = {

A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov random fields using a continuous Gibbs sampler. The Markov random fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a continuous Gibbs sampler enables the algorithm to adapt continuous-valued regional prototypes in a manner analogous to vector quantization while the discrete Gibbs sampler is used to adjust region boundaries.

}, keywords = {adaptive color image segmentation, adaptive signal processing, continuous Gibbs sampler, continuous-valued regional prototypes, discrete Gibbs sampler, global spatial constraints, image colour analysis, image sampling, image segmentation, local spatial constraints, Markov processes, Markov Random Fields, point-based methods, random processes, region boundaries adjustment, spatially-based methods, vector quantization}, doi = {http://dx.doi.org/10.1109/ICIP.2002.1039085}, author = {S Wesolkowski and P Fieguth} } @article {589, title = {K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation}, journal = {Pattern Recognition}, volume = {35}, year = {2002}, pages = {1959 - 1972}, abstract = {

The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This combined K-means and iterative FLD is referred to as the KIF algorithm. Two KIF implementations are presented: a mixture resolving approach is extended to an unsupervised binary hierarchical approach. The same binary hierarchical KIF algorithm is used to properly segment images even though the number of classes, the class spatial boundaries, and the number of samples per class vary. The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions. This unsupervised methodology is demonstrated to be an improvement over other published texture segmentation results using a wide variety of test imagery. Gabor filters and co-occurrence probabilities are used as texture features.

}, keywords = {Binary hierarchical clustering, Co-occurrence probabilities, Computer Vision, Fisher linear discriminant, Gabor filters, image segmentation, K-means, texture segmentation}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/S0031-3203(01)00138-8}, url = {http://www.sciencedirect.com/science/article/pii/S0031320301001388}, author = {D A. Clausi} } @inproceedings {597, title = {A Markov random fields model for hybrid edge and region based colour image segmentation}, booktitle = {15th Canadian Conference on Electrical and Computer Engineering}, year = {2002}, abstract = {

A framework based on a Markov random field approach for color image segmentation enhanced by edge detection is presented. We use a previously developed methodology to transform the image into an R\&$\#$39;G\&$\#$39;B\&$\#$39; space to remove any highlight components preserving the vector-angle component, representing color hue but not intensity, to remove shading effects. To improve the segmentation process we describe the idea of a line process. This allows for the integration of region segmentation with edge detection in a Markov random field framework. We discuss the advantages of this new model with respect to the previously developed image segmentation model.

}, keywords = {color hue, color theory, dichromatic reflection model, edge detection, highlight components removal, hybrid edge-based color image segmentation, image colour analysis, image segmentation, line process, Markov processes, Markov random fields model, random processes, region segmentation, region-based color image segmentation, RGB space, shading effects removal, vector-angle component}, doi = {http://dx.doi.org/10.1109/CCECE.2002.1013070}, author = {S Wesolkowski and P Fieguth} } @inproceedings {531, title = {Image segmentation using MRI vertebral cross-sections}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Computer assisted spinal surgery requires the development of a 3-D image of a patient\&$\#$39;s spine. A method is being developed to construct such a 3-D spinal image from axial MRI cross-sections, using a deformable template. This paper outlines techniques used to register the model with the patient data. First an algorithm based on symmetry of the anatomy is developed to determine the position and orientation of the vertebrae in each cross-section. Second, an active contour is used for segmentation of the spinal cord. Last, a novel edge detection scheme is developed to identify the low contrast edges of the vertebral body in each cross-section

}, keywords = {3D image, 3D spinal image, algorithm, anatomy symmetry, axial MRI cross-sections, biomedical MRI, computer assisted spinal surgery, deformable template, edge detection, image segmentation, low contrast edges identification, medical image processing, MRI vertebral cross-sections, orthopaedics, orthopedic surgery, patient data, patient spine, scale estimation, spinal cord segmentation, surgery, vertebrae orientation, vertebrae position, vertebral body}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933633}, author = {S Booth and D A. Clausi} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and D A. Clausi} } @article {576, title = {Multiscale statistical methods for the segmentation of signals and images}, journal = {IEEE Transactions on Image Processing}, volume = {9}, year = {2000}, pages = {456 - 468}, abstract = {

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired

}, keywords = {biomedical MRI, boundary locations, brain, denoising, error statistics, Gaussian processes, image reconstruction, image segmentation, images, inhomogeneous Gaussian Markov random fields, interference suppression, Markov processes, medical image processing, medical imaging, multiscale methods, noisy image, noisy signal, reconstruction, recursive procedures, scientific applications, Segmentation, signal reconstruction, signals, statistical interpretation, variational approach, variational techniques}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.826782}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} } @inproceedings {491, title = {Multiscale Methods for the Segmentation of Images}, booktitle = {ICASSP {\textquoteright}96}, year = {1996}, abstract = {

This work presents a method for segmenting images based on gradients in the intensity function. Past approaches have centered on formulating the problem in the context of variational calculus as the minimization of a functional involving the image intensity and edge functions. Computational methods for finding the minima of such variational problems are prone to two shortfalls: they are often computationally intensive and almost always incapable of computing error statistics associated with the segmentation. Using a particular variational formulation as a starting point, this paper presents a derivation of an associated statistical formulation using multiscale models. The result is an algorithm which is fast and capable of computing error statistics

}, keywords = {error statistics, functional, functional equations, gradients, image segmentation, images, intensity function, minimisation, minimization, multiscale models, Segmentation, statistical formulation, variational calculus, variational techniques}, doi = {http://dx.doi.org/10.1109/ICASSP.1996.545869}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} }