Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm

<table>Lesion segmentation on a breast MRI scan: (a) locate a rectangle ROI box that contained a postcontrast breast MRI lesion; (b) initial segmentation by the FCMs-based method; (c) deformation of GVF snake using FCMs-based contour for initialization; (d) radiologists’ manual delineation. The average time cost and dynamic memory cost of the method we proposed are 2.4180 seconds and 1256.75 KB.</table>

Computational and Mathematical Methods in Medicine

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Figure 2: Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm 

Figure 2 | Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm 