Abstract
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.
Keywords
- Binary Image
- Mammographic Density
- Segmentation Result
- Local Binary Pattern
- Probability Density Distribution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zwiggelaar, R., Denton, E.R.E. (2006). Texture Based Segmentation. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_58
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DOI: https://doi.org/10.1007/11783237_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
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