Abstract
Breast cancer is the most frightening cancer for women in the world. The current problem that closely related with this issue is how to deal with small calcification part inside the breast called micro calcification (MC). As a preventive way, a breast screening examination called mammogram is provided. Mammogram image with a considerable amount of MC has been a problem for the doctor and radiologist when they should determine correctly the region of interest, in this study is clustered MC. Therefore, we propose to develop an automated method to detect clustered MC utilizing two main methods, multi-branches standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection. Our proposed method was resulting in 70.8% of classification rate, then for the sensitivity and specificity obtained 79% and 87%, respectively. The gained results are adequately promising to be more developed in some areas.
Highlights
Uncontrolled growth of breast cells caused by a genetic abnormality is a short meaning of breast cancer
Selection process has done via labeling method of the image that obtained from subtraction the smoothing image from the contrast enhance image, and classification of features successfully completed by neural network
The data set comes from the Japanese Society of Medical Imaging Technology, and each image has size 2510x2000 pixels and each pixel consists of 10 bits
Summary
Uncontrolled growth of breast cells caused by a genetic abnormality is a short meaning of breast cancer. Breast cancer starts from lobules cells, glands or milk producer and duct cells, part that transporting milk from the lobules to the nipple. We can notice to all regions, the rates of mortality are very high and obviously there is no region in the world that has not affected with this cancer. Selection process has done via labeling method of the image that obtained from subtraction the smoothing image from the contrast enhance image, and classification of features successfully completed by neural network. This method was resulting superfine sensitivity equal with 100% and 87.7% of specificity with proper classification rate 89%. In this study we propose to make a system that can automatically detect the clustered MC based on the strengths from the Tieudeu et al with different enhancement image algorithm combine with detection of individual MC as done by [4] which employed the statistical features to detect the MC
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