Mammogram segmentation using maximal cell strength updation in XXXX .

@entity5 @entity1 @entity465 @entity0
UNASSIGNED: @entity0 is the most frequently diagnosed type of @entity5 among @entity1 . Mammogram is one of the most effective tools for early detection of the @entity0 . Various computer-aided systems have been introduced to detect the @entity0 from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in @entity465 ( @entity465 ). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes @entity465 with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25  % and accuracy of 93.48  %.
