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Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field

by and *,†
Computer Science Department, University of KwaZulu Natal, Durban 4000, South Africa
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2020, 12(8), 1224; https://doi.org/10.3390/sym12081224
Received: 23 May 2020 / Revised: 9 June 2020 / Accepted: 15 June 2020 / Published: 26 July 2020
(This article belongs to the Section Computer and Engineer Science and Symmetry)
Markov random field (MRF) theory has achieved great success in image segmentation. Researchers have developed various methods based on MRF theory to solve skin lesions segmentation problems such as pixel-based MRF model, stochastic region-merging approach, symmetric MRF model, etc. In this paper, the proposed method seeks to provide a complement to the advantages of the pixel-based MRF model and stochastic region-merging approach. This is in order to overcome shortcomings of the pixel-based MRF model, because of various challenges that affect the skin lesion segmentation results such as irregular and fuzzy border, noisy and artifacts presence, and low contrast between lesions. The strength of the proposed method lies in the aspect of combining the benefits of the pixel-based MRF model and the stochastic region-merging by decomposing the likelihood function into the multiplication of stochastic region-merging likelihood function and the pixel likelihood function. The proposed method was evaluated on bench marked available datasets, PH2 and ISIC. The proposed method achieves Dice coefficients of 89.65 % on PH2 and 88.34 % on ISIC datasets respectively. View Full-Text
Keywords: skin lesion; segmentation; Markov random field; stochastic region-merging skin lesion; segmentation; Markov random field; stochastic region-merging
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MDPI and ACS Style

Salih, O.; Viriri, S. Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field. Symmetry 2020, 12, 1224. https://doi.org/10.3390/sym12081224

AMA Style

Salih O, Viriri S. Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field. Symmetry. 2020; 12(8):1224. https://doi.org/10.3390/sym12081224

Chicago/Turabian Style

Salih, Omran; Viriri, Serestina. 2020. "Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field" Symmetry 12, no. 8: 1224. https://doi.org/10.3390/sym12081224

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