Open AccessDetection of basal cell carcinoma using color and histogram measures of semitranslucent areas
Author(s) -
Stoecker William V.,
Gupta Kapil,
Shrestha Bijaya,
Wronkiewiecz Mark,
Chowdhury Raeed,
Stanley R. Joe,
Xu Jin,
Moss Randy H.,
Celebi M. Emre,
Rabinovitz Harold S.,
Oliviero Margarat,
Malters Joseph M.,
Kolm Isabel
Publication year - 2009
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2009.00354.x
Subject(s) - artificial intelligence , histogram , smoothness , basal cell carcinoma , texture (cosmology) , chromaticity , pattern recognition (psychology) , receiver operating characteristic , color histogram , computer science , mathematics , computer vision , medicine , basal cell , color image , image processing , pathology , statistics , image (mathematics) , mathematical analysis
Background: Semitranslucency, defined as a smooth, jelly‐like area with varied, near‐skin‐tone color, can indicate a diagnosis of basal cell carcinoma (BCC) with high specificity. This study sought to analyze potential areas of semitranslucency with histogram‐derived texture and color measures to discriminate BCC from non‐semitranslucent areas in non‐BCC skin lesions. Methods: For 210 dermoscopy images, the areas of semitranslucency in 42 BCCs and comparable areas of smoothness and color in 168 non‐BCCs were selected manually. Six color measures and six texture measures were applied to the semitranslucent areas of the BCC and the comparable areas in the non‐BCC images. Results: Receiver operating characteristic (ROC) curve analysis showed that the texture measures alone provided greater separation of BCC from non‐BCC than the color measures alone. Statistical analysis showed that the four most important measures of semitranslucency are three histogram measures: contrast, smoothness, and entropy, and one color measure: blue chromaticity. Smoothness is the single most important measure. The combined 12 measures achieved a diagnostic accuracy of 95.05% based on area under the ROC curve. Conclusion: Texture and color analysis measures, especially smoothness, may afford automatic detection of BCC images with semitranslucency.