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- Publisher Website: 10.1109/TIP.2019.2899941
- Scopus: eid_2-s2.0-85067348181
- PMID: 30794172
- WOS: WOS:000471715300004
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Article: Automatic Muscle Fiber Orientation Tracking in Ultrasound Images Using a New Adaptive Fading Bayesian Kalman Smoother
Title | Automatic Muscle Fiber Orientation Tracking in Ultrasound Images Using a New Adaptive Fading Bayesian Kalman Smoother |
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Authors | |
Keywords | Ultrasound images muscle fiber orientation region of interest Bayesian Kalman Filter Kalman smoothing |
Issue Date | 2019 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83 |
Citation | IEEE Transactions on Image Processing, 2019, v. 28 n. 8, p. 3714-3727 How to Cite? |
Abstract | This paper proposes a new algorithm for automatic estimation of muscle fiber orientation (MFO) in musculoskeletal ultrasound images, which is commonly used for both diagnosis and rehabilitation assessment of patients. The algorithm is based on a novel adaptive fading Bayesian Kalman filter (AF-BKF) and an automatic region of interest (ROI) extraction method. The ROI is first enhanced by the Gabor filter (GF) and extracted automatically using the revoting constrained Radon transform (RCRT) approach. The dominant MFO in the ROI is then detected by the RT and tracked by the proposed AF-BKF, which employs simplified Gaussian mixtures to approximate the non-Gaussian state densities and a new adaptive fading method to update the mixture parameters. An AF-BK smoother (AF-BKS) is also proposed by extending the AF-BKF using the concept of Rauch-Tung-Striebel smoother for further smoothing the fascicle orientations. The experimental results and comparisons show that: 1) the maximum segmentation error of the proposed RCRT is below nine pixels, which is sufficiently small for MFO tracking; 2) the accuracy of MFO gauged by RT in the ROI enhanced by the GF is comparable to that of using multiscale vessel enhancement filter-based method and better than those of local RT and revoting Hough transform approaches; and 3) the proposed AF-BKS algorithm outperforms the other tested approaches and achieves a performance close to those obtained by experienced operators (the overall covariance obtained by the AF-BKS is 3.19, which is rather close to that of the operators, 2.86). It, thus, serves as a valuable tool for automatic estimation of fascicle orientations and possibly for other applications in musculoskeletal ultrasound images. |
Persistent Identifier | http://hdl.handle.net/10722/293356 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LIU, Z | - |
dc.contributor.author | Chan, SC | - |
dc.contributor.author | Zhang, S | - |
dc.contributor.author | Zhang, Z | - |
dc.contributor.author | Chen, X | - |
dc.date.accessioned | 2020-11-23T08:15:34Z | - |
dc.date.available | 2020-11-23T08:15:34Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2019, v. 28 n. 8, p. 3714-3727 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/293356 | - |
dc.description.abstract | This paper proposes a new algorithm for automatic estimation of muscle fiber orientation (MFO) in musculoskeletal ultrasound images, which is commonly used for both diagnosis and rehabilitation assessment of patients. The algorithm is based on a novel adaptive fading Bayesian Kalman filter (AF-BKF) and an automatic region of interest (ROI) extraction method. The ROI is first enhanced by the Gabor filter (GF) and extracted automatically using the revoting constrained Radon transform (RCRT) approach. The dominant MFO in the ROI is then detected by the RT and tracked by the proposed AF-BKF, which employs simplified Gaussian mixtures to approximate the non-Gaussian state densities and a new adaptive fading method to update the mixture parameters. An AF-BK smoother (AF-BKS) is also proposed by extending the AF-BKF using the concept of Rauch-Tung-Striebel smoother for further smoothing the fascicle orientations. The experimental results and comparisons show that: 1) the maximum segmentation error of the proposed RCRT is below nine pixels, which is sufficiently small for MFO tracking; 2) the accuracy of MFO gauged by RT in the ROI enhanced by the GF is comparable to that of using multiscale vessel enhancement filter-based method and better than those of local RT and revoting Hough transform approaches; and 3) the proposed AF-BKS algorithm outperforms the other tested approaches and achieves a performance close to those obtained by experienced operators (the overall covariance obtained by the AF-BKS is 3.19, which is rather close to that of the operators, 2.86). It, thus, serves as a valuable tool for automatic estimation of fascicle orientations and possibly for other applications in musculoskeletal ultrasound images. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83 | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.rights | IEEE Transactions on Image Processing. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Ultrasound images | - |
dc.subject | muscle fiber orientation | - |
dc.subject | region of interest | - |
dc.subject | Bayesian Kalman Filter | - |
dc.subject | Kalman smoothing | - |
dc.title | Automatic Muscle Fiber Orientation Tracking in Ultrasound Images Using a New Adaptive Fading Bayesian Kalman Smoother | - |
dc.type | Article | - |
dc.identifier.email | Chan, SC: scchan@eee.hku.hk | - |
dc.identifier.authority | Chan, SC=rp00094 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2019.2899941 | - |
dc.identifier.pmid | 30794172 | - |
dc.identifier.scopus | eid_2-s2.0-85067348181 | - |
dc.identifier.hkuros | 319249 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 3714 | - |
dc.identifier.epage | 3727 | - |
dc.identifier.isi | WOS:000471715300004 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1057-7149 | - |