Document Type

Thesis - Open Access

Award Date

2016

Degree Name

Master of Science (MS)

Department

Electrical Engineering and Computer Science

First Advisor

Sung Shin

Keywords

Breast cancer, classification, MRI

Abstract

Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved performance of the proposed shape metric was used to evaluate and to compare the performances between existing method, which is called Circularity Range Ratio and proposed method, which is called Circularity Max. The result shows that the proposed Circularity Max method improves the Matthews Correlation Coefficient, specificity, sensitivity and accuracy. Therefore, the shape metric can be a promising tool to provide preliminary decision support information to physicians for further diagnosis.

Library of Congress Subject Headings

Breast -- Radiography.

Breast -- Imaging.

Breast -- Cancer -- Diagnosis.

Image processing -- Digital techniques.

Image analysis.

Radiography, Medical -- Digital techniques.

Description

Includes bibliographical references (pages 49-53)

Format

application/pdf

Number of Pages

61

Publisher

South Dakota State University

Rights

Copyright © 2016 Tae Keun Heo

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