Fully-automated tongue detection in ultrasound images

• Published in: Computers in biology and medicine 2019-08, Vol.111, p.103335-103335, Article 103335
• Main Authors: Karimi, Elham, Ménard, Lucie, Laporte, Catherine
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Clustering; Contours; Energy; Fully-automated tracking; Image detection; Image enhancement; Image processing; Image segmentation; Kinematics; Methods; Neural networks; Sensors; Shape; Software; Speech; Speeches; Standard deviation; Tongue; Tongue detection; Tracking systems; Training; Ultrasonic imaging; Ultrasound
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2019.103335

Tracking the tongue in ultrasound images provides information about its shape and kinematics during speech. Current methods for detecting/tracking the tongue require manual initialization or training using large amounts of labeled images. In this article, we propose a solution to convert a semi-automatic tongue contour tracking system to a fully-automatic one. This work introduces a new method for extracting tongue contours in ultrasound images that requires no training nor manual intervention. The method consists in an image enhancement step based on phase symmetry, followed by skeletonization and clustering steps, leading to a set of candidate points that can be used to fit an active contour to the image and subsequently initialize a tracking algorithm. Two novel quality measures were also developed that predict the reliability of the segmentation result so that an image with a reliable contour 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. This paper also improves the accuracy of tracking by incorporating two criteria to reset the tracking algorithm from time to time. Experiments show that fully automated and semi-automated methods result in very similar mean sum of distances errors, respectively, indicating that the proposed automatic initialization does not significantly alter accuracy. Moreover, further results show that tracking accuracy is improved when using the new segmentation technique within the proposed re-initialization scheme. •A fully automated method is presented for tongue segmentation in ultrasound images.•The method can be used to initialize tracking without loss of accuracy.•The method can be used to periocially re-initialize tracking, improving accuracy.