Unsupervised skin lesions border detection via two-dimensional image analysis

Authors:
Qaisar Abbas;Irene Fondón;Muhammad Rashid
Affiliations:
Department of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China and Center for Biomedical Imaging and Bioinformatics, Key Laborat ...;Department of Signal Theory and Communications, School of Engineering Path of Discovery, s/n C. P., 41092 Seville, Spain;Department of Life Science and Technology, Biochemistry and Molecular Biology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
Venue:
Computer Methods and Programs in Biomedicine
Year:
2011

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Abstract

The skin cancer was analyzed by dermoscopy helpful for dermatologists. The classification of melanoma and carcinoma such as basal cell, squamous cell, and merkel cell carcinomas tumors can be increased the sensitivity and specificity. The detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems. The artifacts such as, dermoscopy-gel, specular reflection and outline (skin lines, blood vessels, and hair or ruler markings) were also contained in the dermoscopic images. In this paper, we present an unsupervised approach for multiple lesion segmentation, modification of Region-based Active Contours (RACs) as well as artifact diminution steps. Iterative thresholding is applied to initialize level set automatically; the stability of curves is enforced by maximum smoothing constraints on Courant-Friedreichs-Lewy (CFL) function. The work has been tested on dermoscopic database of 320 images. The border detection error is quantified by five distinct statistical metrics and manually used to determine the borders from a dermatologist as the ground truth. The segmentation results were compared with other state-of-the-art methods along with the evaluation criteria. The unsupervised border detection system increased the true detection rate (TDR) is 4.31% and reduced the false positive rate (FPR) of 5.28%.