title: Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images.
abstract: BACKGROUND
  Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective.
  
  
  METHODS
  This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis.
  
  
  RESULTS
  The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87.
  
  
  CONCLUSIONS
  The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space's blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.
authors: Department of Computer Science, University of Central Arkansas, Conway, AR, USA.
authors: Sertan
authors: S
authors: Kaya
authors: Department of Bioinformatics, University of Arkansas-Little Rock, Little Rock, AR, 72204, USA.
authors: Mustafa
authors: M
authors: Bayraktar
authors: Department of Computer Science, University of Central Arkansas, Conway, AR, USA. skockara@uca.edu.
authors: Sinan
authors: S
authors: Kockara
authors: Department of Computer Science and Information Systems, Texas A&M-Commerce, Commerce, TX, USA.
authors: Mutlu
authors: M
authors: Mete
authors: Department of Computer Science, University of Central Arkansas, Conway, AR, USA.
authors: Tansel
authors: T
authors: Halic
authors: Department of Dermatology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
authors: Halle E
authors: HE
authors: Field
authors: Department of Dermatology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
authors: Henry K
authors: HK
authors: Wong
mesh_terms: D000465:Algorithms; D003116:Color; D000068598:Data Accuracy; D046169:Dermoscopy / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000000981:diagnostic imaging* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology