Computer assisted quantification of hyoid bone motion in fluoroscopic videos

Ishtiaque Hossain, Angela Roberts-South, Mandar Jog, Mahmoud R. El-Sakka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Videofluoroscopic Swallowing Study is a technique commonly used by radiologists to detect abnormalities in the swallowing process. While the subject swallows the food, X-ray images are taken and then compiled in a video form. The video is later analyzed by the radiologist using visual means. Since the nature of the inspection is highly subjective, the result of the inspection can barely be reliable. One of the assessed measures is the elevation of the hyoid bone during the swallow. This research introduces a semi-automatic method which identifies the hyoid bone in fluoroscopic videos and quantifies its motion. Before identifying the hyoid bone, the region-of-interest is automatically identified using a classification-based approach and subsequent image processing procedures are applied to the identified region-of-interest. Results show that the proposed method can accurately quantify the motion of the hyoid bone.

Original languageEnglish (US)
Title of host publicationVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages757-761
Number of pages5
StatePublished - 2013
Event8th International Conference on Computer Vision Theory and Applications, VISAPP 2013 - Barcelona, Spain
Duration: Feb 21 2013Feb 24 2013

Publication series

NameVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume1

Other

Other8th International Conference on Computer Vision Theory and Applications, VISAPP 2013
Country/TerritorySpain
CityBarcelona
Period2/21/132/24/13

Keywords

  • Dysphagia
  • Haar classifier
  • Hyoid bone
  • Kinesiologic analysis
  • Object detection
  • Swallowing disorder
  • Template matching
  • Tracking
  • Videofluoroscopic swallowing study

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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