Authors:
Viswanaath Subramanian
1
;
Randy H. Moss
1
;
Ryan K. Rader
2
;
Sneha K. Mahajan
1
and
William V. Stoecker
3
Affiliations:
1
Missouri University of Science & Technology, United States
;
2
Stoecker & Associates, United States
;
3
Missouri University of Science & Technology and Stoecker & Associates, United States
Keyword(s):
Pattern Analysis, Image Processing, Object Detection, Template Matching, Seborrheic Keratosis, Milia-Like Cysts.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Medical Image Applications
;
Shape Representation and Matching
Abstract:
Early detection of melanoma by magnified visible-light imaging (dermoscopy) is hindered by lesions which mimic melanoma. Automatic discrimination of melanoma from mimics could allow detection of melanoma at an earlier stage. Seborrheic keratoses are common mimics; these have distinctive bright structures: starry milia-like cysts (MLCs). We report discrimination of MLCs from mimics by features extracted from starry MLC (star) candidates. After pre-processing, 2D template matching is optimized with respect to star template size, histogram pre-processing, and 2D statistics. The novel aspects of this research were new details for region of interest (ROI) analysis of the centers of the star candidate, a new method for determining shape of hazy objects and multiple template matching, using unprocessed ROIs, shape-limited ROIs, and histogram-equalized ROIs. Features retained in the final model for the decision MLC vs. mimic by logistic regression include star size, 2D first correlation
coefficient, correlation coefficient to the star shape template, equalized correlation coefficient, relative star brightness, and statistical features at the star center. These methods allow optimization of MLC features found by 2D template correlation. This research confirms the importance of fine ROI features and ROI neighborhoods in medical imaging.
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