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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Zwiggelaar, R., & Denton, E. R. E. (2006). Texture based segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4046 LNCS, pp. 433–440). Springer Verlag. https://doi.org/10.1007/11783237_58
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