Title

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

Abstract

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.

Meeting Name

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)

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Comments

This publication was made possible by Grant Number SBIR R44CA-101639-02A2 of the National Institutes of Health (NIH).

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)

978-989-758-225-7

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 SciTePress, All rights reserved.

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