Abstract
The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies. The detection of melanoma in its early stage is essential for prognosis improvement and for guaranteeing a high five-year relative survival rate of patients. The clinical diagnosis of skin lesions is challenging and not trivial since it depends on human vision and physician experience and expertise. Therefore, a computer method that makes an accurate extraction of important details of skin lesion image can assist dermatologists in cancer detection. In particular, the border detection is a critical computer vision issue owing to the wide range of lesion shapes, sizes, colours and skin texture types. In this paper, an automatic and effective pigmented skin lesion segmentation method in dermoscopic image is presented. The proposed procedure is adopted to extract a mask of the lesion region without the adoption of other signal processing procedures for image improvement. A quantitative experimental evaluation has been performed on a publicly available database. The achieved results show the method validity and its high robustness towards irregular boundaries, smooth transition between lesion and skin, noise and artifact presence.
Highlights
OverviewComputer aided diagnosis (CADx) of melanoma (Figure 1) makes an objective evaluation of skin lesions and provides reproducible diagnosis by eliminating the inter and intra-observer variabilities typical of the examination by specialist [1]
The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies
CADx system operates as an automated second opinion or as a double reading system that supports dermatologists in early assessment of skin cancer and in the follow-up of pigmented skin lesions [3]
Summary
Computer aided diagnosis (CADx) of melanoma (Figure 1) makes an objective evaluation of skin lesions and provides reproducible diagnosis by eliminating the inter and intra-observer variabilities typical of the examination by specialist [1]. In this perspective, the aim of CADx systems is to increase clinician performance by helping in the early identification and localization of potential abnormalities [2]. A key step in the development of CADx systems for the automatic diagnosis of skin lesions is the border detection/segmentation phase as it heavily influences the performance that the method can achieve. The input of the procedure is the dermoscopic image-under-test while the output are the segmentation of the skin lesion and a detailed border identification
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