Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC Bioinformatics

Proceedings

Mustafa Bayraktar¹  Mutlu Mete²  Sinan Kockara³  Sertan Kaya³  Tansel Halic³  Halle E. Field⁴  Henry K. Wong⁴ 

[1] Department of Bioinformatics, University of Arkansas-Little Rock, 72204, Little Rock, AR, USA;Department of Computer Science and Information Systems, Texas A&M-Commerce, Commerce, TX, USA;Department of Computer Science, University of Central Arkansas, Conway, AR, USA;Department of Dermatology, University of Arkansas for Medical Sciences, 72205, Little Rock, AR, USA;

关键词: Support Vector Machine;    Support Vector Machine Classifier;    Chain Code;    Texture Feature Extraction;    Radius Size;   

DOI  :  10.1186/s12859-016-1221-4

来源: Springer

【 摘 要 】

BackgroundAutomated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective.MethodsThis study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis.ResultsThe proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87.ConclusionsThe use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space’s blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.

【 授权许可】

CC BY   
© The Author(s). 2016

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