Fully-automated tongue detection in ultrasound images

Abstract

Tracking the tongue in ultrasound images provides information about its shape and kinematics during speech. In this thesis, we propose engineering solutions to better exploit the existing frameworks and deploy them to convert a semi-automatic tongue contour tracking system to a fully-automatic one. Current methods for detecting/tracking the tongue require manual initialization or training using large amounts of labeled images. This work introduces a new method for extracting tongue contours in ultrasound images that requires no training nor manual intervention. The method consists in: (1) application of a phase symmetry filter to highlight regions possibly containing the tongue contour; (2) adaptive thresholding and rank ordering of grayscale intensities to select regions that include or are near the tongue contour; (3) skeletonization of these regions to extract a curve close to the tongue contour and (4) initialization of an accurate active contour from this curve. Two novel quality measures were also developed that predict the reliability of the method so that optimal frames can be chosen to confidently initialize fully automated tongue tracking. This is achieved by automatically generating and choosing a set of points that can replace the manually segmented points for a semi-automated tracking approach. To improve the accuracy of tracking, this work also incorporates two criteria to re-set the tracking approach from time to time so the entire tracking result does not depend on human refinements. Experiments were run on 16 free speech ultrasound recordings from healthy subjects and subjects with articulatory impairments due to Steinert’s disease. Fully automated and semi automated methods result in mean sum of distances errors of 1.01mm±0.57mm and 1.05mm± 0.63mm, respectively, showing that the proposed automatic initialization does not significantly alter accuracy. Moreover, the experiments show that the accuracy would improve with the proposed re-initialization (mean sum of distances error of 0.63mm±0.35mm).

Date	23 Nov 2018
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Catherine Laporte (Supervisor) & Lucie Ménard (Co-supervisor)