Abstract

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Breast cancer is regarded as one of the most frequent mortality causes among women. It is very important to create a system to diagnose suspicious masses in mammograms for early breast cancer detection. In this paper, we propose an automatic breast mass segmentation method based on patch merging method and generalized hierarchical Fuzzy C Means (GHFCM). The patch merging method is used to obtain the adaptive region of interest (ROI), while the GHFCM method which is able to overcome the drawbacks of effect of image noise and Euclidean distance FCM which is sensitive to outliers is used to obtain the precisely mass segmentation results. The new method is evaluated over MiniMIAS dataset. The segmentation performance from experimentations demonstrates that our method outperforms the other compared methods.

Delineating brain tumor boundaries from multi-modality magnetic resonance images (MRIs) is a crucial step in brain cancer surgical and treatment planning. In this paper, we propose a fully automatic technique for brain tumor segmentation from multi-modality human brain MRIs. We first use the intensities of different modalities in MRIs to represent the features of both normal and abnormal tissues. Then, the multiple classifier system (MCS) is applied to calculate the probabilities of brain tumor and normal brain tissue in the whole image. At last, the spatial-contextual information is proposed by constraining the classified neighbors to improve the classification accuracy. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results.