Evaluating lesion segmentation on breast sonography as related to lesion type

Breast sonography currently provides a complementary diagnosis when other modalities are not conclusive. However, lesion segmentation on sonography is still a challenging problem due to the presence of artifacts. To solve these problems, Markov random fields and maximum a posteriori-based methods are used to estimate a distortion field while identifying regions of similar intensity inhomogeneity. In this study, different initialization approaches were exhaustively evaluated using a database of 212 B-mode breast sonograms and considering the lesion types. Finally, conclusions about the relationship between the segmentation results and lesions types are described.