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Research on Techniques of Computer-aided Diagnosis of Breast Cancer for Mammography

Author: XiongJian
Tutor: XuXiangYang
School: Huazhong University of Science and Technology
Course: Computer Software and Theory
Keywords: Breast x-ray image Breast Cancer Computer-aided detection and diagnosis Tumor detection
CLC: TP29-AI
Type: Master's thesis
Year: 2007
Downloads: 78
Quote: 1
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Abstract


Breast cancer is the most common malignancy in older women . Mammography x-ray photography is the most reliable and effective method of early diagnosis of breast cancer . Research based computer-aided detection and diagnosis of breast x-ray image of the breast (Computer-Aided Detect and Diagnosis, CAD) system will help to improve the diagnostic accuracy and objectivity , reduce diagnostic workload . Automatically detect breast lumps include the following process steps : automatic extraction of region of interest , suspicious lumps segmentation , feature extraction and classification of suspicious lumps . First, based on template matching method automatically extract the region of interest ( Region Of Interest ROI ) images to locate the approximate location of the suspicious lumps . Followed by a suspicious lump ROI segmentation , accurate boundary segmentation suspicious lumps first preprocessed to enhance the contrast . Experiment comparing segmentation algorithm based on dynamic programming algorithm and two maximum entropy threshold method , the results show that the former split better , the latter split faster . Finally, with reference to the diagnosis of breast masses of experience of the radiologist , split out suspicious lumps extract the corresponding features , improved k - nearest neighbor classifier suspicious lumps classified as positive or negative , and the contours of the positive area in the original image displayed . The selected breast x-ray image database (Digital Database for Screening Mammography, DDSM) from the University of South Florida in the United States representative of the 155 cases , a total of 306 images , experimental results show that the system sensitivity was 91.4 % , an average of 2.04 false-positive images , to achieve a high detection accuracy .

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