Automatic Dirt Trail Analysis in Dermoscopy Images

Abstract

Background: Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. Methods: In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. Results: For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave-one-out approach. Conclusion: Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation.

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Keywords and Phrases

Analysis algorithms; Automatic Detection; Basal cell carcinoma; Benign lesion; Data sets; Dermoscopy; Dermoscopy images; Dirt trails; Leave-one-out; Network-based; Receiver operating characteristic curves; Skin cancers; Skin lesion; Skin lesion images; Dermatology; Diseases; Image analysis; Neural networks; Diagnosis; algorithm; benign tumor; dirt trail; epiluminescence microscopy; human; major clinical study; receiver operating characteristic; skin color; skin defect; skin examination; tumor volume; Algorithms; Carcinoma; Basal Cell; Differential; Feasibility Studies; Humans; Image Processing; Computer-Assisted; Logistic Models; Models; Theoretical; Neural Networks (Computer); ROC Curve; Skin; Skin Neoplasms; Neural network

International Standard Serial Number (ISSN)

0909-752X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2013 John Wiley & Sons, All rights reserved.

Publication Date

01 Feb 2013

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