Springer, Lecture Notes in Computer Science, p. 9-16, 2008
DOI: 10.1007/978-3-540-70538-3_2
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This paper presents a comparison of two clustering based al- gorithms and one region based algorithm for segmenting fatty and dense tissue in mammographic images. This is a crucial step in order to obtain a quantitative measure of the density of the breast. The first algorithm is a multiple thresholding algorithm based on the excess entropy, the second one is based on the Fuzzy C-Means clustering algorithm, and the third one is based on a statistical analysis of the breast. The performance of the algorithms is exhaustively evaluated using a database of full-field digital mammograms containing 150 CC and 150 MLO images and ROC analysis (ground-truth provided by an expert). Results demonstrate that the use of region information is useful to obtain homogeneous region seg- mentation, although clustering algorithms obtained better sensitivity.