title: Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images.
abstract: OBJECTIVE
  Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation.
  
  
  METHODS
  Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio.
  
  
  RESULTS
  On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics.
  
  
  CONCLUSIONS
  The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work.
authors.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62025, USA.
authors.forename: R
authors.initials: R
authors.lastname: Kaur
authors.affiliation: Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62025, USA.
authors.forename: R
authors.initials: R
authors.lastname: LeAnder
authors.affiliation: Stoecker & Associates, Rolla, MO, 65401, USA.
authors.forename: N K
authors.initials: NK
authors.lastname: Mishra
authors.affiliation: Stoecker & Associates, Rolla, MO, 65401, USA.
authors.forename: J R
authors.initials: JR
authors.lastname: Hagerty
authors.affiliation: Department of Electrical Engineering, University of Bejaia, Bejaia, Algeria.
authors.forename: R
authors.initials: R
authors.lastname: Kasmi
authors.affiliation: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, 65209, USA.
authors.forename: R J
authors.initials: RJ
authors.lastname: Stanley
authors.affiliation: Department of Computer Science, University of Central Arkansas, Conway, AR, 72035, USA.
authors.forename: M E
authors.initials: ME
authors.lastname: Celebi
authors.affiliation: Stoecker & Associates, Rolla, MO, 65401, USA.
authors.forename: W V
authors.initials: WV
authors.lastname: Stoecker
mesh_terms: D000465:Algorithms; D002280:Basal Cell Carcinoma / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D046169:Dermoscopy / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008545:Melanoma / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012878:Skin Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology
keywords: Huang method; Isodata method; Li method; Otsu method; Shanbhag method; basal cell carcinoma; dermoscopy; image analysis; lesion segmentation; skin cancer; thresholding