Ishtiaque Hossain, Angela Roberts-South, Mandar Jog, 
                  and Mahmoud R. El-Sakka, "Semi-automatic Assessment of 
                  Hyoid Bone Motion in Digital Video Fluoroscopic Images",
                  Computer Methods in Biomechanics and Biomedical 
                  Engineering: Imaging & Visualization, Vol. 2, No. 1, 
                  pp. 25-37, 2014
               
               
               
               Abstract
               
               
               
                  The swallowing process involves triggering the movements of a 
                  number of muscles in the throat that transports the food from 
                  the mouth to the stomach successfully and at the same time 
                  prevents it from getting into the airway and the lung. In 
                  order to detect abnormalities in the swallowing process, 
                  radiologists use a technique called Videofluoroscopic 
                  Swallowing Study. It is a video of X-ray images that are 
                  taken while the patient swallows food, which is later 
                  visually inspected by the radiologist to evaluate the 
                  patient's swallowing ability. It has been reported that 
                  measuring the movement of the hyoid bone plays an important 
                  role in the evaluation process. However, due to the 
                  subjective nature of visual inspection, radiologists have 
                  difficulty reaching unanimous decision about the outcome of 
                  the evaluation. In this research, a semi-automatic method is 
                  proposed which tracks the hyoid bone and quantifies its 
                  movement. Using a classification-based approach, the proposed
                  method automatically identifies the region-of-interest before 
                  identifying the hyoid bone. This allows limiting image 
                  processing procedures to the relevant area in the image. 
                  Results show that the proposed method identifies and tracks 
                  the hyoid bone with significant accuracy.