title: Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images.
abstract: 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.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL 62025, USA.
authors.forename: Pelin
authors.initials: P
authors.lastname: Guvenc
authors.forename: Robert W
authors.initials: RW
authors.lastname: LeAnder
authors.forename: Serkan
authors.initials: S
authors.lastname: Kefel
authors.forename: William V
authors.initials: WV
authors.lastname: Stoecker
authors.forename: Ryan K
authors.initials: RK
authors.lastname: Rader
authors.forename: Kristen A
authors.initials: KA
authors.lastname: Hinton
authors.forename: Sherea M
authors.initials: SM
authors.lastname: Stricklin
authors.forename: Harold S
authors.initials: HS
authors.lastname: Rabinovitz
authors.forename: Margaret
authors.initials: M
authors.lastname: Oliviero
authors.forename: Randy H
authors.initials: RH
authors.lastname: Moss
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002280:Basal Cell Carcinoma / Q000473:pathology*; D003116:Color; D003124:Colorimetry / Q000379:methods; D016208:Databases, Factual; D046169:Dermoscopy / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012878:Skin Neoplasms / Q000473:pathology*