Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field
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 on PH2 and on ISIC datasets respectively.
View Full-Text
▼
Show Figures
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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 StyleSalih, 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
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Search more from Scilit