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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Background Ulcers are frequently visible in magnified, cross-polarized, dermoscopy images of basal cell carcinoma. An ulcer without a history of trauma, a so-called “atraumatic” ulcer, is an important sign of basal cell carcinoma, the most common skin cancer. Distinguishing such ulcers from similar features found in benign lesions is challenging. In this research, color and texture features of ulcers are analyzed to discriminate basal cell carcinoma from benign lesions. Methods Ulcers in polarized-light dermoscopy images of 49 biopsy-proven basal cell carcinomas were identified and manually selected. For 153 polarized-light dermoscopy images of benign lesions, those areas which most closely mimicked ulcers were similarly selected. Fifteen measures were analyzed over the areas of ulcers and ulcer mimics. Six of those measures were texture measures: energy, variance, smoothness, skewness, uniformity and entropy. Nine of those measures were color measures: relative measures of red, green and blue; chromaticity of red, green and blue; and the ratios blue-to-green, blue-to-red and green-to-red. Results A back-propagation artificial neural network was able to discriminate most of the basal cell carcinoma from benign lesions, with an area under the ROC curve as high as 92.46%, using color and texture features of ulcer areas. Conclusion Separation of basal cell carcinoma from benign cutaneous lesions by image analysis techniques applied to ulcers is feasible. As ulcers are a critical feature in malignant lesions, this finding may have application in the automatic detection of basal cell carcinoma.