Texture Based Segmentation

The ability of human observers to discriminate between textures is related to the contrast between key structural elements and their repeating patterns. Here we have developed an automatic texture classification approach based on this principle. Local contrast information is modelled and a hybrid metric, based on probability density distributions and transportation estimation, are used to classify unseen samples. Quantitative and qualitative evaluation, based on mammographic images and Wolfe classification, is presented and shows segmentation results in line with the various classes.

[1]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[3]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[4]  K. Pahlavan,et al.  Texture Modeling by Multiple Pairwise Pixel Interactions , 1996 .

[5]  Lei Zhu,et al.  Optimal Mass Transport for Registration and Warping , 2004, International Journal of Computer Vision.

[6]  L. Tabár,et al.  The Tabár classification of mammographic parenchymal patterns. , 1997, European journal of radiology.

[7]  N F Boyd,et al.  Automated analysis of mammographic densities and breast carcinoma risk , 1997, Cancer.

[8]  S. M. Steve.  SUSAN - a new approach to low level image processing , 1997 .

[9]  Constantino Carlos Reyes-Aldasoro,et al.  Volumetric Texture Description and Discriminant Feature Selection for MRI , 2003, IPMI.

[10]  J. Wolfe.  Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[11]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[12]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[13]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[14]  Reyer Zwiggelaar,et al.  EM Texture Segmentation of Mammographic Images , 2003 .

[15]  Andrew Zisserman,et al.  Classifying Images of Materials: Achieving Viewpoint and Illumination Independence , 2002, ECCV.

[16]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[17]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Ferris M Hall.  Mammographic density categories. , 2007, Radiology.

[19]  Luc Van Gool,et al.  Editorial: Special Issue on “Texture Analysis and Synthesi” , 2004, International Journal of Computer Vision.

[20]  Bo Zhang,et al.  An efficient and effective region-based image retrieval framework , 2004, IEEE Transactions on Image Processing.

[21]  Remco C. Veltkamp,et al.  A Pseudo-Metric for Weighted Point Sets , 2002, ECCV.

[22]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ferris M. Hall,et al.  Mammographic density categories. Author's reply , 2002 .

[24]  Matti Pietikäinen,et al.  Image Analysis with Local Binary Patterns , 2005, SCIA.

[25]  Luc Van Gool,et al.  Editorial: Special Issue on “Texture Analysis and Synthesi” , 2005, International Journal of Computer Vision.

[26]  Reyer Zwiggelaar.  Texture based segmentation: automatic selection of co-occurrence matrices , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[27]  F. L. Hitchcock.  The Distribution of a Product from Several Sources to Numerous Localities , 1941 .

[28]  N Karssemeijer,et al.  Automated classification of parenchymal patterns in mammograms. , 1998, Physics in medicine and biology.

[29]  Christopher J. Taylor,et al.  Measuring similarity between pixel signatures , 2002, Image Vis. Comput..