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Document Type

Thesis - University Access Only

Award Date

2014

Degree Name

Master of Science (MS)

Department

Electrical Engineering and Computer Science

First Advisor

Sung Y. Shin

Keywords

Breast Cancer, CAD, Feature extraction, Classification, SVM, GMM, FLS, FGMM, MCC

Abstract

Mammography is potentially a convenient screening method to be comfortable and effective in early detection of breast cancer, but its interpretation is difficult due to low quality images and the limitations of physicians’ experience. Recently, Computer Aided Diagnosis (CADx) in digital mammograms intends to provide assistance to physicians in image interpretation and increase breast cancer diagnosis accuracy. The main objective of this thesis is to enhance and introduce a new CADx system to classify the detected regions in wireless mammogram images into malignant or benign categories. In this approach, different sets of morphological and textural features were investigated and a correlation based feature selection algorithm was used to reduce the CADx system size and complexity. Also, after performing a comparative study to investigate the accuracy of different popular classifier method such as Support Vector Machine (SVM), Gaussian Mixture Model (GMM), and Fuzzy Logic System (FLS), a new classification method called Fuzzy Gaussian Mixture Model (FGMM) by combining GMM and FLS is evaluated and introduced. The experimental results are obtained from a data set of 300 images taken from the Digital Database for Screening Mammography (DDSM, University of South Florida) for different classes. Confusion matrix analysis is used to measure the performance of the CADx system. The results show that the proposed FGMM classifier has achieved an overall classification quality of 86.16%, with 93% accuracy, 90% sensitivity and 96% specificity, and outperformed other classifiers in all aspects. The experimental results obtained from the developed CADx in this research prove that the proposed technique will improve the diagnostic accuracy and reliability of radiologists’ image interpretation in the diagnosis of breast cancer. The resulting breast cancer CADx detection system is a promising tool to provide preliminary decision support information to physicians for further diagnosis.

Library of Congress Subject Headings

Breast -- Imaging
Breast -- Radiography
Breast -- Cancer -- Diagnosis
Diagnostic imaging -- Digital techniques
Fuzzy systems

Description

Includes bibliographic reference (pages 58-61)

Format

application/pdf

Number of Pages

72

Publisher

South Dakota State University

Rights

In Copyright - Non-Commercial Use Permitted
http://rightsstatements.org/vocab/InC-NC/1.0/

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