Title

Automatic Detection of Basal Cell Carcinoma Using Telangiectasia Analysis in Dermoscopy Skin Lesion Images

Abstract

Background: Telangiectasia, dilated blood vessels near the surface of the skin of small, varying diameter, are critical dermoscopy structures used in the detection of basal cell carcinoma (BCC). Distinguishing these vessels from other telangiectasia, that are commonly found in sun-damaged skin, is challenging. Methods: Image analysis techniques are investigated to find vessels structures in BCC automatically. The primary screen for vessels uses an optimized local color drop technique. A noise filter is developed to eliminate false-positive structures, primarily bubbles, hair, and blotch and ulcer edges. From the telangiectasia mask containing candidate vessel-like structures, shape, size and normalized count features are computed to facilitate the discrimination of benign skin lesions from BCCs with telangiectasia. Results: Experimental results yielded a diagnostic accuracy as high as 96.7% using a neural network classifier for a data set of 59 BCCs and 152 benign lesions for skin lesion discrimination based on features computed from the telangiectasia masks. Conclusion: In current clinical practice, it is possible to find smaller BCCs by dermoscopy than by clinical inspection. Although almost all of these small BCCs have telangiectasia, they can be short and thin. Normalization of lengths and areas helps to detect these smaller BCCs.

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Keywords and Phrases

Automatic Detection; Basal Cell Carcinoma; Benign Lesion; Clinical Practices; Data Sets; Dermoscopy; Diagnostic Accuracy; Image Analysis Techniques; Local Color; Neural Network Classifier; Noise Filters; Primary Screen; Skin Lesion; Skin Lesion Images; Telangiectasia; Vessel-like Structures; Vessels; Crystal Structure; Dermatology; Image Analysis; Neural Networks; Blood Vessels; Algorithm; Artificial Neural Network; Benign Skin Tumor; Differential Diagnosis; Epiluminescence Microscopy; Expert System; Human; Skin Blood Vessel; Skin Examination; Neural Network; Algorithms; Carcinoma; Basal Cell; Florida; Humans; Image Interpretation; Computer-assisted; Missouri; Neural Networks (computer); Pattern Recognition; Automated; Reproducibility of Results; Sensitivity and Specificity; Skin Neoplasms; Telangiectasis

International Standard Serial Number (ISSN)

0909-752X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2011 John Wiley & Sons, All rights reserved.

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