Masters Theses

Abstract

"The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms"--Abstract, page iv.

Advisor(s)

Stanley, R. Joe

Committee Member(s)

Stoecker, William V.
Moss, Randy Hays, 1953-

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2016

Pagination

ix, 35 pages

Note about bibliography

Includes bibliographical references (pages 20-29).

Rights

© 2016 Jason R. Hagerty, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Melanoma -- Diagnosis
Skin -- Cancer -- Diagnosis
Image processing

Thesis Number

T 10876

Electronic OCLC #

952592864

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