Breast Cancer Segmentation Using Global Thresholding and Region Merging - Singh, Nidhi; Veenadhari, S (International Journal of Computer Sciences and Engineering, 2018)
Recently, more attention is being given to detect breast cancer in women. But, Due to the lack the diagnostic to suggest whether breast cancer is presented in a person is still a research issue. The proposed work gives a hybrid methodology based on global thresholding and region merging for segmentation of breast cancer in Mammogram Images. In the proposed algorithm we use wiener filtering to remove Gaussian noise then apply image normalization based on histogram shrink to enhance the quality of image. Next, Global thresholding using Otsu’s method is used in order to segment the masses resulting Region of Interest(ROI) and then Region merging is used to extract segmented masses from image. Accuracy rate of the proposed method is 82% and Error rate is only 18%.