The Experts below are selected from a list of 60906 Experts worldwide ranked by ideXlab platform
John Coleman - One of the best experts on this subject based on the ideXlab platform.
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Automatic Segmentation of vocal tract mr images
International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
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ISBI - Automatic Segmentation of vocal tract MR images
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
Zeynab Raeesy - One of the best experts on this subject based on the ideXlab platform.
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Automatic Segmentation of vocal tract mr images
International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
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ISBI - Automatic Segmentation of vocal tract MR images
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
Jayaram K Udupa - One of the best experts on this subject based on the ideXlab platform.
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Automatic Segmentation of vocal tract mr images
International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
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ISBI - Automatic Segmentation of vocal tract MR images
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
Sylvia Rueda - One of the best experts on this subject based on the ideXlab platform.
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Automatic Segmentation of vocal tract mr images
International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
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ISBI - Automatic Segmentation of vocal tract MR images
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Zeynab Raeesy, Sylvia Rueda, Jayaram K Udupa, John ColemanAbstract:Magnetic resonance imaging (MRI) is widely applied as a safe and reliable method in studying the hidden mechanisms of human speech production. Automatic Segmentation of vocal tract shape in MRI is a challenging task due to the dynamic nature of articulation, the variability in the shape introduced by different sounds or different speakers' articulatory configurations, and the connectivity of vocal tract airway to other channels of air such as the nasal tract. A new approach for the Automatic Segmentation of the vocal tract shape in dynamic MR images is proposed. A method of Automatic landmark tagging by recursive boundary subdivision (RBS) is applied to obtain the corresponding sets of landmarks on the vocal tract contours. The oriented active shape model (OASM) technique is adopted to recognise and delineate the shape of the vocal tract in standardised MR images. The results are presented and evaluated both qualitatively and quantitatively. We demonstrate that this is a promising approach for Automatic Segmentation of large databases of vocal tract images for the purposes of speech production studies.
Shrikanth S Narayanan - One of the best experts on this subject based on the ideXlab platform.
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rapid semi Automatic Segmentation of real time magnetic resonance images for parametric vocal tract analysis
Conference of the International Speech Communication Association, 2010Co-Authors: Michael Proctor, Daniel Bone, Nassos Katsamanis, Shrikanth S NarayananAbstract:A method of rapid semi-Automatic Segmentation of real-time magnetic resonance image data for parametric analysis of vocal tract shaping is described. Tissue boundaries are identified by seeking pixel intensity thresholds along tract-normal gridlines. Airway contours are constrained with respect to a tract centerline defined as an optimal path over the graph of all intensity minima between the glottis and lips. The method allows for superimposition of reference boundaries to guide Automatic Segmentation of anatomical features which are poorly imaged using magnetic resonance ‐ dentition and the hard palate ‐ resulting in more accurate sagittal sections than those produced by fully Automatic Segmentation. We demonstrate the utility of the technique in the dynamic analysis of tongue shaping in Tamil liquid consonants. Index Terms: speech production, vocal tract Segmentation, MRI, tongue shaping, articulatory analysis
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INTERSPEECH - Rapid Semi-Automatic Segmentation of Real-time Magnetic Resonance Images for Parametric Vocal Tract Analysis
2010Co-Authors: Michael Proctor, Daniel Bone, Nassos Katsamanis, Shrikanth S NarayananAbstract:A method of rapid semi-Automatic Segmentation of real-time magnetic resonance image data for parametric analysis of vocal tract shaping is described. Tissue boundaries are identified by seeking pixel intensity thresholds along tract-normal gridlines. Airway contours are constrained with respect to a tract centerline defined as an optimal path over the graph of all intensity minima between the glottis and lips. The method allows for superimposition of reference boundaries to guide Automatic Segmentation of anatomical features which are poorly imaged using magnetic resonance ‐ dentition and the hard palate ‐ resulting in more accurate sagittal sections than those produced by fully Automatic Segmentation. We demonstrate the utility of the technique in the dynamic analysis of tongue shaping in Tamil liquid consonants. Index Terms: speech production, vocal tract Segmentation, MRI, tongue shaping, articulatory analysis
