Template Matching for Detection of Starry Milia-Like Cysts in Dermoscopic Images


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.

Meeting Name

8th International Conference on Computer Vision Theory and Applications (2013: Feb. 21-24, Barcelona, Spain)


Electrical and Computer Engineering

Second Department


Keywords and Phrases

Correlation Coefficient; Logistic Regressions; Milia-Like Cysts; Multiple Template Matching; Object Detection; Pattern Analysis; Seborrheic Keratosis; Statistical Features; Dermatology; Diagnosis; Graphic Methods; Image Processing; Logistics; Medical Imaging; Oncology; Optimization; Template Matching; Stars

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Document Type

Article - Conference proceedings

Document Version


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Publication Date

01 Feb 2013