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Thesis - University Access Only
Master of Science (MS)
Electrical Engineering and Computer Science
Sung Y. Shin
The breast mammogram image is one of the most important materials of the Computer-Aided Diagnosis (CAD) system to support diagnosis of breast cancer. In the CAD system, intensity value is a widely used feature for medical image processing. Classification of the breast mammogram image as normal or abnormal class is important, since it supports the early detection of diagnosis of breast cancer to reduce the number of deaths by breast cancer. The main objective of this thesis is to develop improved Harris Corner Detection with improved input training data set for Support Vector Machine (SVM) to classify a breast mammogram image as normal or abnormal. Corner pixels from improved Harris Corner Detection are used as a training input feature for SVM. The experimental results demonstrate that the proposed method achieved higher accuracy and greater performance in execution time.
Library of Congress Subject Headings
Breast -- Imaging.
Breast -- Cancer -- Diagnosis.
Breast -- Radiography
Includes bibliographical references (pages 32-35).
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Kim, Hyun II, "SVM-based Harris Corner Detection to Classify Normal/Abnormal Breast Mammogram Images" (2013). Theses and Dissertations. 1453.