SVM-based Harris Corner Detection to Classify Normal/Abnormal Breast Mammogram Images
Hyun Kim · Open PRAIRIE (South Dakota State University) · 2013
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.