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.
N. K. Mishra and R. Kaur and R. Kasmi and S. Kefel and P. Guvenc and J. G. Cole and J. R. Hagerty and H. Y. Aradhyula and R. LeAnder and R. J. Stanley and R. H. Moss and W. V. Stoecker, "Automatic Separation of Basal Cell Carcinoma from Benign Lesions in Dermoscopy Images with Border Thresholding Techniques," Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2017, Porto, Portugal), vol. 4, pp. 115-123, SciTePress, Feb 2017.
The definitive version is available at https://doi.org/10.5220/0006173601150123
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 (2017: Feb. 27-Mar. 1, Porto, Portugal)
Electrical and Computer Engineering
Keywords and Phrases
Computer graphics; Diagnosis; Image processing; Image segmentation; Regression analysis; Automatic analysis; Automatic diagnostics; Automatic separations; Basal cell carcinoma (BCC); Diagnostic accuracy; Logistic regression analysis; Thresholding segmentation; Thresholding techniques; Computer vision
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2017 SciTePress, All rights reserved.