Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
Sung Y. Shin
Classification, Feature Extraction, Harris Corner Detection, Mammogram Images, Pre-processing, Support Vector Machine
Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as benign and malignant classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is timeconsuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, having a better training data set directly affect the quality of classification process. In this paper, a method is proposed based on supervised learning and automatic thresholding for both generating better training data set, and more accurate classification of the mammogram images into benign/malignant classes. The procedure consists of pre-processing, removing noise, elimination of unwanted objects, features extraction, and classification. A Support Vector Machine (SVM) is used as the supervised model in two phases which are testing and training. Intensity value, auto-correlation matrix value of detected corners, and, energy, are three extracted features used to train the SVM. Experimental results show this method classify images with more accuracy and less execution time compared to the existing method.
Library of Congress Subject Headings
Breast -- Imaging.
Breast -- Radiography.
Image processing -- Digital techniques.
Support vector machines.
Includes bibliographical references (pages 30-34)
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Taheri, Mohammad, "Enhanced Breast Cancer Classification with Automatic Thresholding Using Support Vector Machine and Harris Corner Detection" (2017). Electronic Theses and Dissertations. 1718.