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Detection of soft tissue abnormalities in mammographic images for early diagnosis of breast cancer

University of British Columbia

Treatment of breast cancer is currently effective only if it is detected at an early stage. X-ray mammography is the most effective method for early detection, however, mammographic images are complex. Researchers have been utilizing image processing and image analysis techniques to assist radiologists in their difficult task of detecting tumors in mammographic images. To aid radiologists in earlier detection of breast cancer, a retrospective study of mammograms was conducted. In this pioneer study, screening mammograms taken prior to the detection of a malignant mass were analyzed. The aim is to determine if there exists any signs of cancer development in the screening mammograms prior to the detection of a mass by the radiologist. For 58 biopsy proven breast cancer patients who were diagnosed by identifying a malignant mass in their mammograms, 224 previous screening mammograms were collected. These mammograms were reviewed by an expert radiologist and three regions were marked on each of the two mammographic projections of each case: 1) the region which corresponds to the site in which the malignant mass subsequently developed, 2) a similar normal region on the same mammogram, and 3) the normal region on the previous screening mammogram of the opposite breast which corresponds to region 1. Sixty-two texture and photometric image features were calculated for all the marked areas. A stepwise discriminant analysis found that six of these features best distinguish between the normal and abnormal regions. The best linear classification function resulted in 72% average classification. A t its current stage, the system can be used by a radiologist to examine suspicious patterns in a mammogram. The regions which are flagged by the system have a 72% chance of developing a malignant mass by the time of the next screening. Therefore, further evaluation of these patients (e.g., a screening examination sooner than the usual one year interval) can result in earlier detection of breast cancer. A novel segmentation algorithm for mammogram partitioning based on fuzzy sets theory was also devised. This algorithm considers the fact that malignant masses and parenchymal patterns have unclear and fuzzy boundaries in a mammogram. It also takes into account the effects of neighboring pixels for this segmentation. This algorithm was evaluated in combination with a texture feature extraction step for detection of malignant masses in mammograms. The mass detection scheme resulted in 94.3% true-positive detection rate and 0.24 false-positives per image on a set of 35 mammograms.

Thesis/Dissertation

application/pdf

2009-07-02

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http://hdl.handle.net/2429/9987

Electrical and Computer Engineering

University of British Columbia

UBCV

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