title: Vocal folds analysis using global energy tracking.
abstract: BACKGROUND
  Detection and quantification of oscillatory irregularities in laryngeal videostroboscopy can be particularly difficult for the human expert. Accordingly, there is a wide interest in automated methods for recovering the folds' temporal trajectory. Unfortunately, current methods typically provide only crude glottal measurements.
  
  
  OBJECTIVE
  An automated procedure for consistently tracking the entire vocal folds' boundary in laryngeal stroboscopy videos, even when the glottal opening is closed.
  
  
  METHODS
  A preprocessing frame-by-frame crude midpoint identification is followed by an active contour evolution to detect the global boundary in each frame independently. A global energy active contour is then jointly defined over the entire video sequence, and the full glottal boundary is detected throughout the video via standard energy minimization.
  
  
  RESULTS
  The vocal folds' boundary is accurately tracked in normal and abnormal stroboscopy videos collected in a clinical setting, and that exhibit a varied range of visual characteristics (eg, lighting conditions). A proof-of-concept evaluation based on the analysis of the waveform of the location of points along the boundary separates between a normal and two markedly different abnormal subjects, and automatically provides a hypothesized localization of the abnormality.
  
  
  CONCLUSIONS
  The first method for automatically tracing the temporal trajectory of all points along the vocal folds' boundary in all frames of a stroboscopy video is presented. The approach opens the door for novel analysis of all regions of the contour, which in turn may lead to automated localization of pathologies.
authors.affiliation: Department of Statistics, Hebrew University, Jerusalem, Israel. galel@huji.ac.il
authors.forename: Gal
authors.initials: G
authors.lastname: Elidan
authors.forename: Josef
authors.initials: J
authors.lastname: Elidan
mesh_terms: D001331:Automation; D001696:Biomechanical Phenomena; D006801:Humans; D007828:Laryngoscopy; D008954:Models, Biological; D010699:Phonation; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D049489:Stroboscopy; D013997:Time Factors; D014741:Video Recording; D014827:Vocal Cords / Q000473:pathology / Q000503:physiopathology*; D014832:Voice Disorders / Q000175:diagnosis* / Q000503:physiopathology