title: Evaluation of vermillion border descriptors and relevance vector machines discrimination model for making probabilistic predictions of solar cheilosis on digital lip photographs.
abstract: BACKGROUND
  Solar cheilosis (SC), a common precancer of the lower lip with a high potential to progress to invasive squamous cell carcinoma, presents with characteristic morphological vermillion-skin border alterations, like the border retraction.
  
  
  OBJECTIVE
  To determine robust macro-morphological descriptors of the vermillion border from non-standardized digital photographs and to exploit a probabilistic model for SC recognition in real clinical environments.
  
  
  METHODS
  Lip borders of 150 individuals (75 SC patients, 75 non-SC controls) were quantified on the basis of the extent of vermillion retraction and the degree of border irregularity employing fractal features and type-P Fourier descriptors. Eight lip border quantifiers were evaluated in terms of their reliability and reproducibility. The probabilistic 'diagnostic' model was implemented using the relevance vector machine (RVM) algorithm.
  
  
  RESULTS
  Picture acquisition contributes substantially to overall variability of lip border images; however, for the different lip descriptors 33% to 65% of border morphological variability is due to differences among individuals. Different camera operators or the use of cameras with different specifications did not affect significantly the extracted lip features. The proposed RVM probabilistic model yielded a high sensitivity and specificity of 94.6% and 96%, respectively.
  
  
  CONCLUSIONS
  We explored the use of digital photography within the clinical routine setting to validate a probabilistic model for the assessment of lip conditions like SC. The proposed method opens new perspectives toward a cost effective, non-invasive monitoring of SC to support large scale epidemiological and interventional studies in different clinical environments.
authors.affiliation: Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, Ioannina 45110, Greece. Electronic address: pspyrid@cc.uoi.gr.
authors.forename: Panagiota
authors.initials: P
authors.lastname: Spyridonos
authors.affiliation: Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, Ioannina 45110, Greece. Electronic address: Ggaitan@cc.uoi.gr.
authors.forename: Georgios
authors.initials: G
authors.lastname: Gaitanis
authors.affiliation: Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, Ioannina 45110, Greece. Electronic address: ibassuka@cc.uoi.gr.
authors.forename: Ioannis D
authors.initials: ID
authors.lastname: Bassukas
authors.affiliation: Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, Ioannina 45110, Greece. Electronic address: mtzaphli@uoi.gr.
authors.forename: Margaret
authors.initials: M
authors.lastname: Tzaphlidou
mesh_terms: D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008046:Lip / Q000473:pathology*; D008048:Lip Neoplasms / Q000473:pathology*; D008297:Male; D010781:Photography; D011230:Precancerous Conditions / Q000473:pathology*; D060388:Support Vector Machine
keywords: Actinic cheilitis; Image analysis; Lip features; Relevance vector machines; Solar cheilosis