SVM-based Harris Corner Detection to Classify Normal/Abnormal Breast Mammogram Images

<p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis - University Access Only

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Year dc:date.available

2013

Author dc:creator

• Kim, Hyun II

Contributors dc:contributor

• Sung Y. Shin

• Civil and Environmental Engineering

Statement dc:rights

• <p>In Copyright - Educational Use Permitted<br /><a href="http://rightsstatements.org/vocab/InC-EDU/1.0/">http://rightsstatements.org/vocab/InC-EDU/1.0/</a></p>

Language dc:language

en

Repository record dc:identifier

https://openprairie.sdstate.edu/etd/1453

OAI identifier oai:identifier

oai:openprairie.sdstate.edu:etd-2452