Based on Fuzzy Linear Discriminant Analysis for Breast Cancer Mammography Analysis
Yu-Shun Cho, Chiun-Li Chin, Kun-Ching Wang · 2011
According to a research report by the Department of Health, Executive Yuan, R.O.C. (Taiwan), breast cancer is the most common type of cancer in women, while the mortality rate of breast cancer of females over 40 years old is extremely high. If detected early, it can be treated early, and the mortality rate of breast cancer can be reduced. Therefore, the image processing technology has been adopted to automatically breast images, select suspicious regions, and provide alerts to assist in doctors' diagnosis, reduce misdiagnosis rates due to fatigue of doctors, and improve diagnostic accuracy. In order to assist physicians in clinical diagnosis, a set of breast cancer detection algorithm was designed in this paper through the Fuzzy theory and linear discriminant analysis(LDA). First, the images were automatically segmented, and then targeting this feature, the brightness values of suspicious regions were retained while the brightness of other areas was reduced to find the suspected location of the cancer. After that, with the images obtained, Law's Mask and grayscale value momentum-intensive technologies were adopted to find the texture of each area. Finally, the Fuzzy LDA was adopted to identify the texture of the cancers in order to capture images of the cancer areas. The experimental results show that the accuracy of the current detection methods can be improved to generate a breast cancer detection system used in the auxiliary medical diagnosis system, effectively reduce physicians' determination time, and improve accuracy.