Lesion Segmentation in Dermoscopy Images Using Particle Swarm Optimization and Markov Random Field

2017 ◽  
Author(s):  
Khalid Eltayef ◽  
Yongmin Li ◽  
Xiaohui Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Random Field ◽  
Markov Random Field ◽  
Particle Swarm ◽  
Lesion Segmentation ◽  
Swarm Optimization ◽  
Markov Random

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

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1224
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Skin Lesion ◽  
Random Field ◽  
Markov Random Field ◽  
Likelihood Function ◽  
Skin Lesions ◽  
Great Success ◽  
Region Merging ◽  
Lesion Segmentation ◽  
Low Contrast ◽  
Markov Random

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.

scholarly journals Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field

2015 ◽  
Vol 45 ◽  
pp. 102-111 ◽  
Author(s):  
Pallab Kanti Roy ◽  
Alauddin Bhuiyan ◽  
Andrew Janke ◽  
Patricia M. Desmond ◽  
Tien Yin Wong ◽  
...  
Keyword(s):  
White Matter ◽  
Random Field ◽  
Markov Random Field ◽  
White Matter Lesion ◽  
Lesion Segmentation ◽  
Markov Random ◽  
Contrast Enhanced

scholarly journals Skin Lesion Segmentation Techniques for Melanoma Diagnosis: Comparative Studies

2021 ◽  
Author(s):  
Farzad Nowroozipour
Keyword(s):  
Particle Swarm Optimization ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Particle Swarm ◽  
Region Growing ◽  
Image Clustering ◽  
Support Vector ◽  
Lesion Segmentation ◽  
Swarm Optimization ◽  
Seeded Region Growing

In recent years, melanoma skin cancer has been one of the rapidest risings of all cancers, which has a high risk of spread. This deadliest form of skin cancer must be diagnosed early for effective treatment. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the segmentation of skin lesion. In this research, we create different new algorithms for the skin lesion segmentation in dermoscopic images. The segmentation algorithms compared are a modified automatic Seeded Region Growing based on Particle swarm optimization image clustering which was used for breast MRI Tumours segmentation, Generalized rough fuzzy c-means algorithm which has been used for brain MR image segmentation, a Support Vector Machine (SVM) and Self-Organizing Map (SOM) with Genetic Algorithm. We used two different datasets with their masks to evaluate the accuracy, sensitivity, and specificity of various segmentation techniques. The results shows that a modified automatic Seeded Region Growing based on Particle swarm optimization image clustering has the highest accuracy (92%) compares with the other algorithms.

scholarly journals Skin Lesion Segmentation Techniques for Melanoma Diagnosis: Comparative Studies

2021 ◽  
Author(s):  
Farzad Nowroozipour
Keyword(s):  
Particle Swarm Optimization ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Particle Swarm ◽  
Region Growing ◽  
Image Clustering ◽  
Support Vector ◽  
Lesion Segmentation ◽  
Swarm Optimization ◽  
Seeded Region Growing

In recent years, melanoma skin cancer has been one of the rapidest risings of all cancers, which has a high risk of spread. This deadliest form of skin cancer must be diagnosed early for effective treatment. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the segmentation of skin lesion. In this research, we create different new algorithms for the skin lesion segmentation in dermoscopic images. The segmentation algorithms compared are a modified automatic Seeded Region Growing based on Particle swarm optimization image clustering which was used for breast MRI Tumours segmentation, Generalized rough fuzzy c-means algorithm which has been used for brain MR image segmentation, a Support Vector Machine (SVM) and Self-Organizing Map (SOM) with Genetic Algorithm. We used two different datasets with their masks to evaluate the accuracy, sensitivity, and specificity of various segmentation techniques. The results shows that a modified automatic Seeded Region Growing based on Particle swarm optimization image clustering has the highest accuracy (92%) compares with the other algorithms.

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