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Accurate segmentation of breast lesions is a crucial step in evaluating thecharacteristics of tumors. However, this is a challenging task, since breastlesions have sophisticated shape, topological structure, and variation in theintensity distribution. In this paper, we evaluated the performance of threeunsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesionsegmentation, namely, Gaussian Mixture Model clustering, K-means clustering anda marker-controlled Watershed transformation based method. All methods wereapplied on breast MRI slices following selection of regions of interest (ROIs)by an expert radiologist and evaluated on 106 subjects' images, which include59 malignant and 47 benign lesions. Segmentation accuracy was evaluated bycomparing our results with ground truth masks, using the Dice similaritycoefficient (DSC), Jaccard index (JI), Hausdorff distance and precision-recallmetrics. The results indicate that the marker-controlled Watershedtransformation outperformed all other algorithms investigated.

Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation