  Accurate segmentation of breast lesions is a crucial step in evaluating the
characteristics of tumors. However, this is a challenging task, since breast
lesions have sophisticated shape, topological structure, and variation in the
intensity distribution. In this paper, we evaluated the performance of three
unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion
segmentation, namely, Gaussian Mixture Model clustering, K-means clustering and
a marker-controlled Watershed transformation based method. All methods were
applied on breast MRI slices following selection of regions of interest (ROIs)
by an expert radiologist and evaluated on 106 subjects' images, which include
59 malignant and 47 benign lesions. Segmentation accuracy was evaluated by
comparing our results with ground truth masks, using the Dice similarity
coefficient (DSC), Jaccard index (JI), Hausdorff distance and precision-recall
metrics. The results indicate that the marker-controlled Watershed
transformation outperformed all other algorithms investigated.
