@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} } @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 {442, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {443, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {444, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {441, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @article {812, title = {Grey level co-occurrence integrated algorithm (GLCIA): a superior computational method to rapidly determine co-occurrence probability texture features}, journal = {Computers \& Geosciences}, volume = {29}, year = {2003}, pages = {837 - 850}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. In this paper, the design, implementation, and testing of a more efficient algorithm to perform this task are presented. This algorithm, known as the grey level co-occurrence integrated algorithm (GLCIA), is a dramatic improvement on earlier implementations. This algorithm is created by integrating the preferred aspects of two algorithms: the grey level co-occurrence hybrid structure (GLCHS) and the grey level co-occurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated two-dimensional data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient one-dimensional data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. Using a variety of test parameters, experiments indicate that the GLCIA requires a fraction (27-54\%) of the computational time compared to using the GLCHS alone. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large digital images.

}, keywords = {texture analysis}, issn = {0098-3004}, doi = {http://dx.doi.org/10.1016/S0098-3004(03)00089-X}, url = {http://www.sciencedirect.com/science/article/pii/S009830040300089X}, author = {D A. Clausi and Y Zhao} } @article {574, title = {Designing Gabor filters for optimal texture separability}, journal = {Pattern Recognition}, volume = {33}, year = {2000}, pages = {1835 - 1849}, abstract = {

The discrimination of textures is a critical aspect of identification in digital imagery. Texture features generated by Gabor filters have been increasingly considered and applied to image analysis. Here, a comprehensive classification and segmentation comparison of different techniques used to produce texture features using Gabor filters is presented. These techniques are based on existing implementations as well as new, innovative methods. The functional characterization of the filters as well as feature extraction based on the raw filter outputs are both considered. Overall, using the Gabor filter magnitude response given a frequency bandwidth and spacing of one octave and orientation bandwidth and spacing of 303 augmented by a measure of the texture complexity generated preferred results.

}, keywords = {Classification Segmentation, Feature Extraction, Gabor filter, texture analysis}, author = {D A. Clausi and E Jernigan} }