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This paper describes an algorithm to segment mammographic 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. © Springer-Verlag Berlin Heidelberg 2006.

Type

Conference paper

Publication Date

01/01/2006

Volume

4046 LNCS

Pages

609 - 615