@article{(Open Science Index):https://publications.waset.org/pdf/9437,
	  title     = {Skin Lesion Segmentation Using Color Channel Optimization and Clustering-based Histogram Thresholding},
	  author    = {Rahil Garnavi and  Mohammad Aldeen and  M. Emre Celebi and  Alauddin Bhuiyan and  Constantinos Dolianitis and  George Varigos},
	  country	= {},
	  institution	= {},
	  abstract     = {Automatic segmentation of skin lesions is the first step
towards the automated analysis of malignant melanoma. Although
numerous segmentation methods have been developed, few studies
have focused on determining the most effective color space for
melanoma application. This paper proposes an automatic segmentation
algorithm based on color space analysis and clustering-based histogram
thresholding, a process which is able to determine the optimal
color channel for detecting the borders in dermoscopy images. The
algorithm is tested on a set of 30 high resolution dermoscopy images.
A comprehensive evaluation of the results is provided, where borders
manually drawn by four dermatologists, are compared to automated
borders detected by the proposed algorithm, applying three previously
used metrics of accuracy, sensitivity, and specificity and a new metric
of similarity. By performing ROC analysis and ranking the metrics,
it is demonstrated that the best results are obtained with the X and
XoYoR color channels, resulting in an accuracy of approximately
97%. The proposed method is also compared with two state-of-theart
skin lesion segmentation methods.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {3},
	  number    = {12},
	  year      = {2009},
	  pages     = {365 - 373},
	  ee        = {https://publications.waset.org/pdf/9437},
	  url   	= {https://publications.waset.org/vol/36},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 36, 2009},