Abstract
This paper describes an algorithm to segment mammo- graphic images into regions corresponding to different densities. The breast parenchymal segmentation uses information extracted for statistical texture based classification which is in turn incorporated in multi-vector Markov Random Fields. Such segmentation is key to developing quantitative mammographic analysis. The algorithm’s performance is evaluated quantitatively and qualitatively and the results show the feasibility of segmenting different mammographic densities.
Keywords
- Segmentation Algorithm
- Mammographic Density
- Digital Mammography
- Parenchymal Pattern
- Iterate Conditional Mode
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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Petroudi, S., Brady, M. (2006). Breast Density Segmentation Using Texture. 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_82
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DOI: https://doi.org/10.1007/11783237_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
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