Automatic Separation of Basal Cell Carcinoma from Benign Lesions in Dermoscopy Images with Border Thresholding Techniques

Basal cell carcinoma (BCC), with an incidence in the US exceeding 2.7 million cases/year, exacts a significant toll in morbidity and financial costs. Earlier BCC detection via automatic analysis of dermoscopy images could reduce the need for advanced surgery. In this paper, automatic diagnostic algorithms are applied to images segmented by five thresholding segmentation routines. Experimental results for five new thresholding routines are compared to expert-determined borders. Logistic regression analysis shows that thresholding segmentation techniques yield diagnostic accuracy that is comparable to that obtained with manual borders. The experimental results obtained with algorithms applied to automatically segmented lesions demonstrate significant potential for the new machine vision techniques.

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