Breast Density Segmentation: A Comparison of Clustering and Region Based Techniques

Authors:
A. Torrent;A. Bardera;A. Oliver;J. Freixenet;I. Boada;M. Feixes;R. Martí;X. Lladó;J. Pont;E. Pérez;S. Pedraza;J. Martí
Affiliations:
Computer Vision and Robotics Group, University of Girona, Catalonia, Spain;Graphics & Imaging Laboratory, University of Girona, Catalonia, Spain;Computer Vision and Robotics Group, University of Girona, Catalonia, Spain;Computer Vision and Robotics Group, University of Girona, Catalonia, Spain;Graphics & Imaging Laboratory, University of Girona, Catalonia, Spain;Graphics & Imaging Laboratory, University of Girona, Catalonia, Spain;Computer Vision and Robotics Group, University of Girona, Catalonia, Spain;Computer Vision and Robotics Group, University of Girona, Catalonia, Spain;Department of Radiology, Hospital Josep Trueta of Girona, Catalonia, Spain;Department of Radiology, Hospital Josep Trueta of Girona, Catalonia, Spain;Department of Radiology, Hospital Josep Trueta of Girona, Catalonia, Spain;Computer Vision and Robotics Group, University of Girona, Catalonia, Spain
Venue:
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Year:
2008

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Abstract

This paper presents a comparison of two clustering based algorithms 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 segmentation, although clustering algorithms obtained better sensitivity.