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Comput Med Imaging Graph. 2016 Sep;52:89-103. doi: 10.1016/j.compmedimag.2016.05.002. Epub 2016 May 7.

Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.

Author information

1
Department of Computer, Control, and Management Engineering, Sapienza University of Rome, via Ariosto 25, Rome, Italy; Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium.
2
Department of Computer, Control, and Management Engineering, Sapienza University of Rome, via Ariosto 25, Rome, Italy. Electronic address: bloisi@diag.uniroma1.it.
3
Department of Computer, Control, and Management Engineering, Sapienza University of Rome, via Ariosto 25, Rome, Italy.
4
Istituto Dermopatico dell'Immacolata IDI-IRCCS, via Monti di Creta 104, Rome, Italy.
5
Istituto Dermopatico dell'Immacolata IDI-IRCCS, via Monti di Creta 104, Rome, Italy; Department of Dermatology (Service of Allergology and Clinical Immunology), Cantonal Hospital of Bellinzona, 6500 Bellinzona, Switzerland.

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.

KEYWORDS:

Automatic segmentation; Border detection; Dermoscopy images; Melanoma detection

[Indexed for MEDLINE]

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