Open AccessSector expansion and elliptical modeling of blue‐gray ovoids for basal cell carcinoma discrimination in dermoscopy images
Author(s) -
Guvenc Pelin,
LeAnder Robert W.,
Kefel Serkan,
Stoecker William V.,
Rader Ryan K.,
Hinton Kristen A.,
Stricklin Sherea M.,
Rabinovitz Harold S.,
Oliviero Margaret,
Moss Randy H.
Publication year - 2013
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/srt.12006
Subject(s) - ellipse , segmentation , artificial intelligence , basal cell carcinoma , pattern recognition (psychology) , computer science , feature (linguistics) , gray (unit) , basal cell , mathematics , medicine , geometry , pathology , radiology , linguistics , philosophy
Background Blue‐gray ovoids (B‐ GO s), 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‐ GO s can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B‐ GO s, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B‐ GO s from their benign mimics. Methods Contact dermoscopy images of 68 confirmed BCC s with B‐ GO s 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‐ GO s and mimicking structures. Results Logistic regression analysis determined that blue chromaticity was the best feature for discriminating true B‐ GO s 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‐ GO s in BCC s from similar structures in benign images.