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Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images

Authors


Address:

Dr Robert W. LeAnder

Department of Electrical and Computer Engineering

Southern Illinois University

Edwardsville, 62025 IL

USA

Tel: (618) 650-2524

e-mail: bobleander1@gmail.com

Abstract

Background

Blue-gray ovoids (B-GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B-GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B-GOs from their benign mimics.

Methods

Contact dermoscopy images of 68 confirmed BCCs with B-GOs were obtained. Another set of 131 contact dermoscopic images of benign lesions possessing B-GO mimics provided a benign competitive set. A total of 22 B-GO features were analyzed for all structures: 21 color features and one size feature. Regarding segmentation, this study utilized a novel sector-based, non-recursive segmentation method to expand the masks applied to the B-GOs and mimicking structures.

Results

Logistic regression analysis determined that blue chromaticity was the best feature for discriminating true B-GOs in BCC from benign, mimicking structures. Discrimination of malignant structures was optimal when the final B-GO border was approximated by a best-fit ellipse. Using this optimal configuration, logistic regression analysis discriminated the expanded and fitted malignant structures from similar benign structures with a classification rate as high as 96.5%.

Conclusions

Experimental results show that color features allow accurate expansion and localization of structures from seed areas. Modeling these structures as ellipses allows high discrimination of B-GOs in BCCs from similar structures in benign images.

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