Feature choice for detection of cancerous masses by constrained optimization

This paper reports a progress in the research on detection of cancerous masses with an evidential constrained optimization method. This method performs unsupervised partitioning mammograms into homogeneous regions by using `generic' labels. Domain knowledge is employed to forbid certain configurations of regions during segmentation to reduce the false alarm rate. A constrained stochastic relaxation algorithm is used for building an optimal label map to separate tissue and masses. At the heart of this mammogram partitioning procedure is an evidential disparity measure function that estimates the similarity of two blocks of pixels in the feature space. The specific objective of the research described in this paper is the selection of independent features that represent the difference between tissue and masses texture more adequately for any type of the lesions and give the best segmentation result being combined in the disparity measure. Three types of features have been selected as the result of our experiments: the fractal dimension, a vector computed from pixel values, and a vector computed from the coefficients of Gabor expansion of the pixel block. Experiments with the MIAS database have been conducted and shown the feasibility of utilization of these features.