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
Recommended Citation
Hagerty, Jason R., "Using adaptive thresholding and skewness correction to detect gray areas in melanoma in situ images" (2016). Masters Theses. 7506.
https://scholarsmine.mst.edu/masters_theses/7506