Document Detail


Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images.
MedLine Citation:
PMID:  23020816     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Blue-gray ovoids (B-GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B-GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B-GOs from their benign mimics.
METHODS: Contact dermoscopy images of 68 confirmed BCCs with B-GOs were obtained. Another set of 131 contact dermoscopic images of benign lesions possessing B-GO mimics provided a benign competitive set. A total of 22 B-GO features were analyzed for all structures: 21 color features and one size feature. Regarding segmentation, this study utilized a novel sector-based, non-recursive segmentation method to expand the masks applied to the B-GOs and mimicking structures.
RESULTS: Logistic regression analysis determined that blue chromaticity was the best feature for discriminating true B-GOs in BCC from benign, mimicking structures. Discrimination of malignant structures was optimal when the final B-GO border was approximated by a best-fit ellipse. Using this optimal configuration, logistic regression analysis discriminated the expanded and fitted malignant structures from similar benign structures with a classification rate as high as 96.5%.
CONCLUSIONS: Experimental results show that color features allow accurate expansion and localization of structures from seed areas. Modeling these structures as ellipses allows high discrimination of B-GOs in BCCs from similar structures in benign images.
Authors:
Pelin Guvenc; Robert W LeAnder; Serkan Kefel; William V Stoecker; Ryan K Rader; Kristen A Hinton; Sherea M Stricklin; Harold S Rabinovitz; Margaret Oliviero; Randy H Moss
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2012-10-01
Journal Detail:
Title:  Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)     Volume:  19     ISSN:  1600-0846     ISO Abbreviation:  Skin Res Technol     Publication Date:  2013 Feb 
Date Detail:
Created Date:  2013-01-08     Completed Date:  2013-06-13     Revised Date:  2014-03-27    
Medline Journal Info:
Nlm Unique ID:  9504453     Medline TA:  Skin Res Technol     Country:  England    
Other Details:
Languages:  eng     Pagination:  e532-6     Citation Subset:  IM    
Copyright Information:
© 2012 John Wiley & Sons A/S.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artificial Intelligence*
Carcinoma, Basal Cell / pathology*
Color
Colorimetry / methods
Databases, Factual
Dermoscopy / methods*
Diagnosis, Differential
Humans
Logistic Models
Models, Biological*
Neoplasms / pathology
Pattern Recognition, Automated / methods*
Skin Neoplasms / pathology*
Grant Support
ID/Acronym/Agency:
CA101639-02A2/CA/NCI NIH HHS; R43 CA153927/CA/NCI NIH HHS; R43 CA153927-01/CA/NCI NIH HHS
Comments/Corrections

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