title: Spatial fuzzy c-means algorithm with adaptive fuzzy exponent selection for robust vermilion border detection in healthy and diseased lower lips.
abstract: BACKGROUND
  Accurate lip contour identification is demanding since variations in color, form and surface texture, even in normal lips, introduce artifacts in non-adapted segmentation algorithms. Herein, a method for vermilion border detection and quantification in healthy and diseased lower lips is presented.
  
  
  OBJECTIVE
  To quantify the morphological irregularities of lower lip border, to validate its discriminative power in solar cheilosis diagnosis and to provide supportive tools toward, cost effective, non invasive, disease monitoring.
  
  
  METHODS
  Segmentation algorithm for lower lip border was based on spatial fuzzy c-means clustering algorithm with adaptive selection of fuzzy exponent m. Lip features measuring morphological lip border deviations were estimated. The method of lip border extraction and quantitative description was evaluated in a gold standard set of 25 young volunteers without onset of lip diseases. Quantitative descriptors were evaluated in terms of correct classification rates in differentiating 30 healthy control cases from 41 patients with solar cheilosis and were further applied to quantify the therapeutic outcome after immunocryosurgery in eight patients with solar cheilosis.
  
  
  RESULTS
  Adaptive estimation of fuzzy exponent m substantially boosted the segmentation quality in gold standard cases yielding quite smooth lip contours and uniformly low values of lip irregularity features. Discriminant analysis highlighted the distance between the extracted and modeled vermilion border as a feature with excellent diagnostic accuracy (sensitivity and specificity 98% and 93% respectively). Results on patients with solar cheilosis followed up after treatment with immunocryosurgery showed that proposed quantitative lip marker was able to trace the improvement of disease after treatment.
  
  
  CONCLUSIONS
  Correct lip border recognition is the prerequisite for extracting essential morphological descriptors from lips with epithelial diseases like solar cheilosis. In this paper we presented an efficient method for the automatic identification and quantitative description of lower lip vermilion border morphology in health and disease using digital photography and image analysis techniques.
authors.affiliation: Department of Medical Physics, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece. Electronic address: pspyrid@cc.uoi.gr.
authors.forename: Panagiota
authors.initials: P
authors.lastname: Spyridonos
authors.affiliation: Department of Skin and Venereal Diseases, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece.
authors.forename: Georgios
authors.initials: G
authors.lastname: Gaitanis
authors.affiliation: Department of Medical Physics, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece. Electronic address: mtzaphli@uoi.gr.
authors.forename: Margaret
authors.initials: M
authors.lastname: Tzaphlidou
authors.affiliation: Department of Skin and Venereal Diseases, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece.
authors.forename: Ioannis D
authors.initials: ID
authors.lastname: Bassukas
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002613:Cheilitis / Q000175:diagnosis* / Q000473:pathology; D016000:Cluster Analysis; D003452:Cryosurgery; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008046:Lip / Q000502:physiology* / Q000503:physiopathology*; D008047:Lip Diseases / Q000175:diagnosis* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D055815:Young Adult
keywords: Actinic cheilitis; Fuzzy clustering; Image analysis; Immunocryosurgery; Lip detection; Solar cheilosis