@article {218, title = {An adaptive Monte Carlo approach to phase-based multimodal image registration}, journal = {IEEE Transactions on Information Technology in Biomedicine}, volume = {14}, year = {2010}, pages = {173 - 179}, abstract = {

In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.

}, keywords = {adaptive Monte Carlo scheme, adaptive sampling approach, Algorithms, Computer-Assisted, Diagnostic Imaging, geometric transformation, Humans, image interpretation, image phase moments, image processing, image registration, iterative methods, Least-Squares Analysis, medical image processing, Monte Carlo Method, Monte Carlo methods, multidimensional sampling distribution, multidimensional solution space, multiresolution algorithm, optimal image alignment, Pearson type VII error, phase based multimodal image registration, random solution candidate generation, sampling methods}, issn = {1089-7771}, doi = {http://dx.doi.org/10.1109/TITB.2009.2035693}, author = {A Wong} } @article {221, title = {Generalized probabilistic scale space for image restoration}, journal = {IEEE Transactions on Image Processing}, volume = {19}, year = {2010}, pages = {2774 - 2780}, abstract = {

A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.

}, keywords = {2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2010.2048973}, author = {A Wong and A Mishra} } @inproceedings {330, title = {Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)}, booktitle = {30th Annual Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2008}, month = {08/2008}, address = {Vancouver, British Columbia, Canada}, abstract = {

A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, Diagnostic Imaging, Humans, image enhancement, image interpretation, Information Storage and Retrieval, Markov Chains, Models, Pattern Recognition, Reproducibility of Results, Statistical, Visible Human Projects}, doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649855}, author = {A Mishra and A Wong and W Zhang and D A. Clausi and P Fieguth} } @inproceedings {331, title = {Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets}, booktitle = {30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2008}, month = {08/2008}, address = {Vancouver, British Columbia, Canada}, abstract = {

This paper presents a novel shape-guided active contour based approach for segmenting and tracking lumbar vertebrae in video fluoroscopy using complex-valued wavelets. representations. Due to low radiation exposure levels, fluoroscopic images are characterized by low signal-to-noise ratios, low contrast resolution, and illumination non-homogeneities both spatially and temporally, making current methods ill-suited for segmenting and tracking lumbar vertebrae based on existing energy functionals. Furthermore, current methods perform poorly in situations characterized by high curvature as found in the structure of lumbar spine vertebrae. In this paper, a novel iterative estimation approach is used to determine an external energy functional based on complex wavelets. A shaped-guided algorithm is used to evolve the contour around a lumbar spine vertebra based on the complex wavelet energy. The high curvature exhibited by the lumbar spine vertebra is addressed through a novel importance sampling scheme. Experimental results show that the proposed algorithm achieves significantly better segmentation and tracking performance for lumbar spine vertebrae in fluoroscopic images when compared to existing techniques.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Female, Fluoroscopy, Humans, Lumbar Vertebrae, Male, Pattern Recognition, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity and Specificity, Video Recording, Young Adult}, doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649290}, author = {A Wong and A Mishra and P Fieguth and D A. Clausi and N M. Dunk and J Callaghan} } @inproceedings {349, title = {Watershed deconvolution for cell segmentation}, booktitle = {30th Annual Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2008}, abstract = {

Cell segmentation and/or localization is the first stage of a (semi)automatic tracking system. We addressed the cell localization problem in our previous work where we characterized a typical blood stem cell in a microscopic image as an approximately circular object with dark interior and bright boundary. We also addressed the modelling of adjacent and dividing cells in our previous work as a deconvolution method to model individual blood stem cell as well as adjacent and dividing blood stem cells where an optimization algorithm was combined with a template matching method to segment cell regions and locate the cell centers. Our previous cell deconvolution method is capable of modelling different cell types with changes in the model parameters. However in cases where either a complex parameterized shape is needed to model a specific cell type, or in place of cell center localization, an exact cell segmentation is needed, this method will not be effective. In this paper we propose a method to achieve cell boundary segmentation. Considering cell segmentation as an inverse problem, we assume that cell centers are located in advance. Then, the cell segmentation will be solved by finding cell regions for optimal representation of cell centers while a template matching method is effectively employed to localize cell

}, keywords = {Algorithms, Artificial Intelligence, Automated, Cells, Computer-Assisted, Cultured, hematopoietic stem cells, Humans, image enhancement, image interpretation, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity}, doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649168}, author = {N Nezamoddin Kachouie and P Fieguth and E Jervis} } @article {356, title = {Extended-Hungarian-JPDA: Exact single-frame stem cell tracking}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {54}, year = {2007}, pages = {2011 - 2019}, abstract = {

The fields of bioinformatics and biotechnology rely on the collection, processing and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. Thus, cell tracking is of crucial importance in the study of cell behaviour and in drug and disease research. Such a multitarget tracking is essentially an assignment problem, NP-hard, with the solution normally found in practice in a reduced hypothesis space. In this paper we introduce a novel approach to find the exact association solution over time for single-frame scan-back stem cell tracking. Our proposed method employs a class of linear programming optimization methods known as the Hungarian method to find the optimal joint probabilistic data association for nonlinear dynamics and non-Gaussian measurements. The proposed method, an optimal joint probabilistic data association approach, has been successfully applied to track hematopoietic stem cells.

}, keywords = {Algorithms, Animals, biocellular images, bioinformatics, biological techniques, biotechnology, cell motility, cell shape, cell size, Cells, cellular biophysics, Computer-Assisted, Data Interpretation, extended-Hungarian-joint probabilistic data association, hematopoietic stem cells, Humans, image enhancement, image interpretation, linear programming optimization methods, Microscopy, multitarget tracking, nonGaussian measurements, nonlinear dynamics, optimal joint probabilistic data association, principal component analysis, Reproducibility of Results, Sensitivity and Specificity, single-frame scan-back stem cell tracking, Statistical, Video}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2007.895747}, author = {N Nezamoddin Kachouie and P Fieguth} } @article {416, title = {Modeling emotional content of music using system identification}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics}, volume = {36}, year = {2005}, pages = {588 - 599}, abstract = {

Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9\% for valence and 78.4\% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.

}, keywords = {arousal dimension, Artificial Intelligence, Automated, average R/sup 2/ statistic, Computer Simulation, emotion recognition, Emotions, Humans, linear model structure, Models, music, music emotional content modeling, Pattern Recognition, Psychological, Psychometrics, system identification, time series, valence dimension}, issn = {1083-4419}, doi = {http://dx.doi.org/10.1109/TSMCB.2005.862491}, author = {M Korhonen and D A. Clausi and E Jernigan} }