Intelligent Breast Tumor Detection System with iterative Fuzzy Linear Discriminant Analysis

• Main Authors: Yu-Shun, 卓育順
• Other Authors: 秦群立
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/64314973051577402144

碩士 === 中山醫學大學 === 應用資訊科學學系碩士班 === 100 === 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. Because the gray value of the breast tumor images are higher than the other normal tissues and the texture of the breast tumor images are very obvious, we aimed at the two characteristics and propsed a algorithms for it. First, we deal the Mammography image of breast with image projection to eliminate the other images which we do not need. Next, we partition the image to many blocks which the sizes are the same. The size of each block conform the size which we need when doing feature extraction, and aim at the block to detect. If all of the pixel values in the block are zero, we will not do the characteristics extraction of it. It is express that there is no breast information in this block, and the remaining blocks characteristics extraction block. In the part of the feature extraction, we use the four characteristics to detect the breast tumor. The features are Laws’ Mask which is the representation of the texture and Histogram which is the representation of the momentum analysis of grayscale value intensity charts and GLCM and the Average Distance which is the representation of the gray value. We aimed at each ROI image block to extract the four features, but we do not use each features of the four features. According to the previous experiment, we will have high successful rate by using the feature of Laws’ Mask and the feature of modification average distance. we use neuron network and iterative fuzzy linear discriminant analysis to judge the tumor. In the part of the experiment results, we use three data base to prove our algorithm, and we also have the estimation and proof from radiographers and doctors who we cooperated. Thus, we will gain the best successful rate of the system identification. The success practicing of the project will improve the accuracy of the existed detection methods. It will produce a breast detection system to assist medical diagnosis and decrease the time of the judgment effective by doctors.