Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images

Pegah Kharazmi; Sunil Kalia; Harvey Lui; Z. Jane Wang; Tim K. Lee

doi:10.1117/12.2293353

27 February 2018 Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images

Pegah Kharazmi, Sunil Kalia, Harvey Lui, Z. Jane Wang, Tim K. Lee

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105750O (2018) https://doi.org/10.1117/12.2293353
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Basal cell carcinoma (BCC) is the most common type of skin cancer, which is highly damaging to the skin at its advanced stages and causes huge costs on the healthcare system. However, most types of BCC are easily curable if detected at early stage. Due to limited access to dermatologists and expert physicians, non-invasive computer-aided diagnosis is a viable option for skin cancer screening. A clinical biomarker of cancerous tumors is increased vascularization and excess blood flow. In this paper, we present a computer-aided technique to differentiate cancerous skin tumors from benign lesions based on vascular characteristics of the lesions. Dermoscopy image of the lesion is first decomposed using independent component analysis of the RGB channels to derive melanin and hemoglobin maps. A novel set of clinically inspired features and ratiometric measurements are then extracted from each map to characterize the vascular properties and blood content of the lesion. The feature set is then fed into a random forest classifier. Over a dataset of 664 skin lesions, the proposed method achieved an area under ROC curve of 0.832 in a 10-fold cross validation for differentiating basal cell carcinomas from benign lesions.

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Citation Download Citation

Pegah Kharazmi, Sunil Kalia, Harvey Lui, Z. Jane Wang, and Tim K. Lee "Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images ", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750O (27 February 2018); https://doi.org/10.1117/12.2293353

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