Reliability of Sonomyography for Pectoralis Major Thickness Measurement

Koo, Terry K.; Wong, C. Jerry; Zheng, Yongping

doi:10.1016/j.jmpt.2010.05.009

Cited by 27 publications

(27 citation statements)

References 43 publications

(45 reference statements)

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“…However, MT measurement is traditionally based on manual selection of 2 reference points at the superficial and deep aponeuroses and usage of an on-screen caliper to obtain the MT frame by frame [31-34], which is time-consuming and subjective. There have been several studies targeting at automatic estimation of MT, such as the Revoting Hough Transform (RVHT) method [35,36], Radon Transform [37], Region of Interest (ROI) [24] and cross-correlation algorithm [38]. Taking RVHT as an example, with assumption that the superficial and deep aponeuroses could be located using RVHT as the very first 2 lines detected, the MT could be computed readily as the distance between them.…”

Section: Introductionmentioning

confidence: 99%

Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

Han

Chen

et al. 2013

BioMed Eng OnLine

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BackgroundUltrasonography is a convenient technique to investigate muscle properties and has been widely used to look into muscle functions since it is non-invasive and real-time. Muscle thickness, a quantification which can effectively reflect the muscle activities during muscle contraction, is an important measure for musculoskeletal studies using ultrasonography. The traditional manual operation to read muscle thickness is subjective and time-consuming, therefore a number of studies have focused on the automatic estimation of muscle fascicle orientation and muscle thickness, to which the speckle noises in ultrasound images could be the major obstacle. There have been two popular methods proposed to enhance the hyperechoic regions over the speckles in ultrasonography, namely Gabor Filtering and Multiscale Vessel Enhancement Filtering (MVEF).MethodsA study on gastrocnemius muscle is conducted to quantitatively evaluate whether and how these two methods could help the automatic estimation of the muscle thickness based on Revoting Hough Transform (RVHT). The muscle thickness results obtained from each of the two methods are compared with the results from manual measurement, respectively. Data from an aged subject with cerebral infarction is also studied.ResultsIt’s shown in the experiments that, Gabor Filtering and MVEF can both enable RVHT to generate comparable results of muscle thickness to those by manual drawing (mean ± SD, 1.45 ± 0.48 and 1.38 ± 0.56 mm respectively). However, the MVEF method requires much less computation than Gabor Filtering.ConclusionsBoth methods, as preprocessing procedure can enable RVHT the automatic estimation of muscle thickness and MVEF is believed to be a better choice for real-time applications.

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Section: Introductionmentioning

confidence: 99%

Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

Han

Chen

et al. 2013

BioMed Eng OnLine

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“…Other rehabilitative ultrasound studies involved manual positioning and holding of the ultrasound probe. [35][36][37][38][39] Our apparatus measured T with the probe at the same orientation and preload. Thus, ICCs of our thickness measurements were superior to other studies.…”

Section: Discussionmentioning

confidence: 99%

A Mechano-Acoustic Indentor System for In Vivo Measurement of Nonlinear Elastic Properties of Soft Tissue

Koo¹,

Cohen²,

Zheng³

2011

Journal of Manipulative and Physiological Therapeutics

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“…Zheng et al used sonomyography to describe the real-time change of muscle thickness detected using B-mode ultrasound images during its dynamic contraction [23] and proposed to use it for prosthetic control [20], [21]. Koo et al used cross-correlation to track the locations of aponeuroses and measure muscle thickness on the ultrasound images [24]. They placed two windows on the superficial and deep aponeuroses and muscle thickness was calculated as the distance between the horizontal center lines of two windows.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tracking of Aponeuroses and Estimation of Muscle Thickness in Ultrasonography: A Feasibility Study

Ling

Zhou

Chen

et al. 2013

IEEE J. Biomed. Health Inform.

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Muscle thickness measurement in ultrasonography was traditionally conducted by a trained operator, and the manual detecting process is time consuming and subjective. In this paper, we proposed an automatic tracking strategy to achieve the continuous and quantitative measurement for gastrocnemius muscle thickness in ultrasound images. The method involved three steps: tracking of seed points, contours extraction of aponeuroses, and muscle thickness estimation. In an ultrasound image sequence, we first selected two seed points in the first frame manually for the superficial and deep aponeuroses, respectively. Seed points in all following frames were then tracked by registering to their respective previous frames. Second, we adopted the local and global intensity fitting model to extract the contours of aponeuroses. At last, the muscle thickness was achieved by calculating the distance between the contours of superficial and deep aponeuroses. The performance of the algorithm was evaluated using 500 frames of ultrasound images. It was demonstrated in the experiments that the proposed methods could be used for objective tracking of aponeuroses and estimation of muscle thickness in musculoskeletal ultrasound images.

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Reliability of Sonomyography for Pectoralis Major Thickness Measurement

Cited by 27 publications

References 43 publications

Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

A Mechano-Acoustic Indentor System for In Vivo Measurement of Nonlinear Elastic Properties of Soft Tissue

Automatic Tracking of Aponeuroses and Estimation of Muscle Thickness in Ultrasonography: A Feasibility Study

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