Paper Info
Title
Lesion Segmentation In Dermoscopy Images Using Particle Swarm Optimization And Markov Random Field
Abstract
Malignant melanoma is one of the most rapidly increasing cancers globally and it is the most dangerous form of human skin cancer. Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma. Early detection of melanoma can be helpful and usually curable. Due to the difficulty for dermatologists in the interpretation of dermoscopy images, Computer Aided Diagnosis systems can be very helpful to facilitate the early detection. The automated detection of the lesion borders is one of the most important steps in dermoscopic image analysis. In this paper, we present a fully automated method for melanoma border detection using image processing techniques. The hair and several noises are detected and removed by applying a bank of directional filters and Image Inpainting method respectively. A hybrid method is developed by combining Particle Swarm Optimization and Markov Random Field methods, in order to delineate the border of the lesion area in the images. The method was tested on a dataset of 200 dermoscopic images, and the experimental results show that our method is superior in terms of the accuracy of drawing the lesion borders compared to alternative methods.
Year
DOI
Venue
2017
10.1109/CBMS.2017.26
2017 IEEE 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)
Keywords
Field
DocType
Markov Random Field, Particle Swarm Optimization, image segmentation, dermoscopy images, melanoma, skin lesion detection
Particle swarm optimization,Computer vision,Imaging modalities,Computer science,Markov random field,Computer-aided diagnosis,Image processing,Inpainting,Image segmentation,Artificial intelligence,Lesion segmentation
Conference
ISSN
Citations 
PageRank 
2372-9198
1
0.37
References 
Authors
16
3
Name
Order
Citations
PageRank
Khalid Eltayef111.05
Yongmin Li231717.07
Xiaohui Liu35042269.99