Enhanced airway-tissue boundary segmentation for real-time magnetic resonance imaging data

This paper introduces an algorithm for robust segmentation of airway-tissue boundaries in the upper airway images recorded by the real-time magnetic resonance imaging. Compared to the previous method by Proctor et al. [1], the present algorithm performs image quality enhancement, including pixel sensitivity correction and grainy noise reduction, followed by robust estimation of airway path between the vocal tract walls. The airway path as well as the locations of the lips and the top of the larynx are found using the Viterbi algorithm. The tissue-airway boundaries are found for each grid line by searching the closest pixel of higher intensity than a threshold from the the estimated airway path. The accuracy of the tissue boundary segmentation was evaluated in terms of root-mean-squared-error as well as statistics (mean and standard deviation) of error for specific region in the vocal tract. Results suggest that the proposed algorithm shows significantly less estimation error than the previous method [1], especially for the front cavity and the inner boundary.