Abstract：Breast MRI image segmentation is a challenging issue. This paper presents a 3D segmentation method, which is based on spatial FCM clustering and Markov random field. First, the MRI image was coarse segmented by spatial FCM to extract lesion contours. And then MRF segmentation was conducted to refine the result. We combined the 3D information of lesion in the MRF process by using segmentation result of contiguous slices to constraint the slice segmentation. At the same time, a membership matrix of FCM segmentation result is used for adaptive adjustment of Markov parameters in MRF segmentation process. The segmentation performance of this method was compared with that of spatial FCM, level set and fuzzy MRF on a database including 50 breast DCE-MRI examinations. Results show that average overlap rate for benign and malignant of our method is 76.4% and 75.5% respectively, compared with
68.7% and 69.5% for spatial FCM, 70.8% and 72.6% for level set method,and 72.9% and 73.6% for fuzzy MRF. It is demonstrated that our method has a better performance in accuracy. In addition, we used unsupervised evaluation method to evaluate the segmentation result of all the 175 breast DCE-MRI image sequences in the database, The uniformities of intra region (URs) for both benign and malignant lesion was more than 0.92. The differences within region (DRs) of 92% of the benign lesions and 98% of the malignant lesions were less than 150, the differences of the inter region (DIR) of 87% of benign lesion were more than 0.25, while those of 90% of malignant lesion were over 0.3. The results demonstrated the proposed method has a good performance in segmentation accuracy.
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