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Automatic dirt trail analysis in dermoscopy images.

Beibei Cheng¹, R Joe Stanley, William V Stoecker

¹Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)

|January 12, 2012

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An algorithm can automatically detect "dirt trails" in dermoscopic images to help diagnose basal cell carcinoma (BCC), the most common skin cancer. This technology aids in the early detection of BCC, improving patient outcomes.

Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Basal cell carcinoma (BCC) is the most prevalent cancer in the United States.
Dermoscopy aids in early BCC detection by identifying specific skin lesion structures.
Dirt trails, characterized by clustered dots and clods, are a newly identified BCC-related structure.

Purpose of the Study:

To develop and evaluate an algorithm for detecting and analyzing dirt trails in dermoscopic images.
To utilize dirt trail characteristics for the automated differentiation of BCC from benign skin lesions.

Main Methods:

An algorithm was developed to extract, measure, and characterize dirt trails based on size, distribution, and color.
A neural network classifier was trained and tested on dermoscopic images of BCC and benign lesions.

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Main Results:

The algorithm successfully identified dirt trails in dermoscopic images.
A neural network classifier achieved an area under the receiver operating characteristic curve of 0.902 using a leave-one-out validation method.

Conclusions:

Automatic detection of dirt trails in dermoscopic images for BCC diagnosis is feasible.
This automated approach can assist in the early and challenging detection of BCC.