title: Adaptable texture-based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma.
abstract: BACKGROUND
  Pink blush is a common feature in basal cell carcinoma (BCC). A related feature, semitranslucency, appears as smooth pink or orange regions resembling skin color. We introduce an automatic method for detection of these features based on smoothness and brightness. We also introduce a neighborhood correction method for texture area correction.
  
  
  METHODS
  Smoothness and brightness were analyzed over four bands: luminance, red, green, and blue, then merged using variance-based dynamic thresholding. Dermoscopic images of 100 biopsy-proven BCCs and 254 competitive benign mimics were used to train the algorithm. Sixteen color and texture features were extracted from the automatically detected areas. The confusion matrix for the algorithm showed 15 classification errors in the training set for the 354 images: three errors in the BCC set and 12 errors in the benign set.
  
  
  RESULTS
  Logistic regression analysis on a separate 1024-image test set was able to achieve good separation of BCC from benign lesions with an area under the receiver operating characteristic curve (ROC) of 0.878 and 0.877 using manually-created and automatically-generated BCC border masks, respectively.
  
  
  CONCLUSIONS
  This pilot study indicates that automatic detection of semitranslucent and pink blush areas in BCC is feasible using colors and first-order texture statistics.
authors.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL, USA.
authors.forename: S
authors.initials: S
authors.lastname: Kefel
authors.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL, USA.
authors.forename: S
authors.initials: S
authors.lastname: Pelin Kefel
authors.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL, USA. rleande@siue.edu.
authors.forename: R W
authors.initials: RW
authors.lastname: LeAnder
authors.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL, USA.
authors.forename: R
authors.initials: R
authors.lastname: Kaur
authors.affiliation: Department of Electrical Engineering, University of Bejaia, Bejaia, Algeria.
authors.forename: R
authors.initials: R
authors.lastname: Kasmi
authors.affiliation: Stoecker & Associates, Rolla, MO, USA.
authors.forename: N K
authors.initials: NK
authors.lastname: Mishra
authors.affiliation: Stoecker & Associates, Rolla, MO, USA.|School of Medicine, University of Missouri, Columbia, MO, USA.
authors.forename: R K
authors.initials: RK
authors.lastname: Rader
authors.affiliation: Stoecker & Associates, Rolla, MO, USA.
authors.forename: J G
authors.initials: JG
authors.lastname: Cole
authors.affiliation: Stoecker & Associates, Rolla, MO, USA.
authors.forename: Z T
authors.initials: ZT
authors.lastname: Woolsey
authors.affiliation: Stoecker & Associates, Rolla, MO, USA.|School of Medicine, University of Missouri, Columbia, MO, USA.
authors.forename: W V
authors.initials: WV
authors.lastname: Stoecker
mesh_terms: D000368:Aged; D000465:Algorithms; D002280:Basal Cell Carcinoma / Q000000981:diagnostic imaging* / Q000473:pathology; D003116:Color; D003124:Colorimetry / Q000379:methods*; D046169:Dermoscopy / Q000379:methods*; D005246:Feedback; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology
keywords: automatic detection; basal cell carcinoma; color; dermoscopy; image analysis; pink blush; segmentation; semitranslucency; texture