Skin lesion image segmentation using Delaunay Triangulation for melanoma detection. (September 2016)

• Record Type: Journal Article
• Authors: Pennisi, Andrea; Bloisi, Domenico D.; Nardi, Daniele; Giampetruzzi, Anna Rita; Mondino, Chiara; Facchiano, Antonio
• Is Part Of: Computerized medical imaging and graphics. Volume 52(2016)
• Page Start: 89
• Page End: 103
• Publication Date: 2016-09
• Subjects: Melanoma detection -- Dermoscopy images -- Automatic segmentation -- Border detection; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.05.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Graphical abstract: Abstract : Highlights: An accurate and fully-automatic method for skin lesion segmentation is proposed. Skin detection and Delaunay Triangulation are used for finding the lesion area. A publicly available data set of dermoscopic images is used for the experiments. Very accurate segmentation results can be obtained for common and atypical nevi. Classification experiments achieved a sensitivity of 93.5%. Abstract: Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.