Document Detail


Segmentation of skin lesions from digital images using joint statistical texture distinctiveness.
MedLine Citation:
PMID:  24658246     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Melanoma is the deadliest form of skin cancer. Incidence rates of melanoma have been increasing, especially among non-Hispanic white males and females, but survival rates are high if detected early. Due to the costs for dermatologists to screen every patient, there is a need for an automated system to assess a patient's risk of melanoma using images of their skin lesions captured using a standard digital camera. One challenge in implementing such a system is locating the skin lesion in the digital image. A novel texture-based skin lesion segmentation algorithm is proposed. A set of representative texture distributions are learned from an illumination-corrected photograph and a texture distinctiveness metric is calculated for each distribution. Next, regions in the image are classified as normal skin or lesion based on the occurrence of representative texture distributions. The proposed segmentation framework is tested by comparing lesion segmentation results and melanoma classification results to results using other state-of-art algorithms. The proposed framework has higher segmentation accuracy compared to all other tested algorithms.
Authors:
Jeffrey Glaister; Alexander Wong; David A Clausi
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  61     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2014 Apr 
Date Detail:
Created Date:  2014-03-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1220-30     Citation Subset:  IM    
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