Automatic contour tracking in ultrasound images

Abstract

In this paper, a new automatic contour tracking system, EdgeTrak, for the ultrasound image sequences of human tongue is presented. The images are produced by a head and transducer support system (HATS). The noise and unrelated high-contrast edges in ultrasound images make it very difficult to automatically detect the correct tongue surfaces. In our tracking system, a novel active contour model is developed. Unlike the classical active contour models which only use gradient of the image as the image force, the proposed model incorporates the edge gradient and intensity information in local regions around each snake element. Different from other active contour models that use homogeneity of intensity in a region as the constraint and thus are only applied to closed contours, the proposed model applies local region information to open contours and can be used to track partial tongue surfaces in ultrasound images. The contour orientation is also taken into account so that any unnecessary edges in ultrasound images will be discarded. Dynamic programming is used as the optimisation method in our implementation. The proposed active contour model has been applied to human tongue tracking and its robustness and accuracy have been verified by quantitative comparison analysis to the tracking by speech scientists.

A study of laryngeal gestures in Mandarin citation tones using simultaneous laryngoscopy and laryngeal ultrasound (SLLUS)

“…Although automated algorithms exist for edge-detection in lingual ultrasound videos (e.g. EdgeTrak: Li, Kambhamettu & Stone 2005), these often require manual monitoring to be fully successful (e.g. McCormick, Frisch & Wodzinski 2008), which makes them only semi-automated.…”

Phonological and phonetic properties of nasal substitution in Sasak and Javanese

“…Mean squared distance and RMSD values are used for various purposes in ultrasound tongue position research, e.g., comparing native and non-native speakers' articulations (Li et al, 2005;Davidson, 2005;Berry et al, 2012), understanding coarticulation (Irfana & Sreedevi, 2016), and evaluating the accuracy of edge-detection algorithms (Roussos et al, 2009;Fasel & Berry, 2010;Csapó & Lulich, 2015). For our purposes, low RMSD values indicate that two sound tokens were articulated with high similarity, whereas high RMSDs indicate that the tokens were articulated with quite distinct lingual contours.…”

“…EdgeTrak software (Li et al, 2005) was used to determine the boundary edges (corresponding to the surface of the tongue) and fit a smoothed graphical spline curves onto the boundaries; edges were hand-corrected as needed. The data for each spline were exported as a set of 100 equidistant coordinate points.…”

Comparative Morphology of the Hominin and African Ape Hyoid Bone, a Possible Marker of the Evolution of Speech

“…Ultrasound gives a high-rate, detailed representation of the entire midsaggital tongue contour (see fig. 1), although segmenting and tracking it is difficult because of artifacts such as noise, invisibility of tongue parts, bone shadows or sound reflection (Li et al, 2005). Other techniques have their own limitations, such as radiation exposure (X-ray film) or large acoustic noise and low rate (MRI).…”

“…Li et al (2005) performed tongue motion averaging to provide best representation of speech motion from several repetitions.…”