A Supervised Air-Tissue Boundary Segmentation Technique in Real-Time Magnetic Resonance Imaging Video Using a Novel Measure of Contrast and Dynamic Programming

Abstract

This paper introduces a technique for the supervised segmentation of Air-Tissue Boundaries (ATBs) in the upper airway of the vocal tract in the real time magnetic resonance imaging (rtMRI) videos. The proposed technique uses a novel measure of contrast across a boundary using Fisher discriminant function. ATBs in all frames of an rtMRI video are jointly estimated by maximizing the proposed measure of contrast around the predicted ATBs and incorporating a smoothness constraint to ensure the ATBs in consecutive frames do not change drastically. Dynamic programming is used for this purpose. The accuracy of the proposed technique is evaluated separately for the upper and lower ATBs using the Dynamic Time Warping distance between the predicted and the ground truth contours. Experiments with rtMRI videos from four subjects show that the error in ATB prediction using the proposed technique is 8.99% less than that using a semi-supervised grid based segmentation approach. A key feature of the proposed approach is that it can reliably predict the ATB outside the vocal tract unlike those with the existing methods.