Masters Theses
Abstract
"Early detection of malignant melanoma greatly benefits patients, as the overall success is dependent on finding these melanomas before they reach the invasive stage. Dermoscopy is a non-invasive skin imaging technique that studies have shown can improve the diagnostic accuracy of dermatologists by as much as 30% over clinical examination. In this project machine vision and image analysis techniques are used to detect annular granular areas in dermoscopy images automatically. The proposed algorithm utilizes the luminance ratio between annular and granular areas within the darkest 30% of the lesion. All points whose luminance value are less than 30% of the histogram are considered for further processing. The method has used some preprocessing steps to remove the unwanted effect of luminance reflection, to extract hair and bubble from the lesion image and to enhance the contrast of the image. Then the lesion plane is searched to find the center and border of annular-granular areas. Statistical analysis has shown that the implemented algorithm has the highest 92 percent in correct detection of annular granular areas"--Abstract, page iii.
Advisor(s)
Moss, Randy Hays, 1953-
Stoecker, William V.
Committee Member(s)
Stanley, R. Joe
Grant, Steven L.
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Electrical Engineering
Publisher
Missouri University of Science and Technology
Publication Date
2012
Pagination
viii, 46 pages
Note about bibliography
Includes bibliographical references (pages 44-45).
Rights
© 2012 Parivash Hajiyani, 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 10552
Print OCLC #
903604830
Electronic OCLC #
903649673
Link to Catalog Record
Recommended Citation
Hajiyani, Parivash, "Automatic detection of annular-granular patterns in melanoma in situ dermoscopy images" (2012). Masters Theses. 7355.
https://scholarsmine.mst.edu/masters_theses/7355