Masters Theses

Keywords and Phrases

Feature extraction; Image processing; K means Segmentation; Melanoma detection

Abstract

"Malignant melanoma is responsible for 75% of the deaths caused due to skin cancer annually. However, melanoma detection can be possible through feature extraction and pattern classification, which can lower the risk, if the melanoma is detected at an early stage. Clustering is one of the most useful tools used to differentiate features that can contribute to melanoma. This research work uses the k-means clustering algorithm for implementation of color segmentation. However, k-means clustering requires a predefined value of k, i.e., the number of clusters must be specified at the beginning of the run. This research uses a predefined value of k=4 determined empirically after numerous test runs on the data set. A set of 888 dermoscopy skin lesion images were used in this work with k-means segmentation used to segment the lesion area in each image into four colors, and 226 features were extracted from each image. Forward stepwise logistic regression (as implemented in the Statistical Analysis Software (SAS) package) was used for feature selection and model building. SAS returned 90 significant features and created a model with a diagnostic accuracy as measured by the area under the Receiver Operating Characteristic (ROC) curve of 0.902"--Abstract, page iii.

Advisor(s)

Moss, Randy Hays, 1953-

Committee Member(s)

Stanley, R. Joe
Stoecker, William V.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2013

Pagination

viii, 39 pages

Note about bibliography

Includes bibliographical references (page 38).

Rights

© 2013 Snigdha Priya Bommadevara, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Melanoma -- DiagnosisSkin -- Cancer -- DiagnosisPattern recognition systems

Thesis Number

T 10631

Electronic OCLC #

912419125

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