The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform

Sergey V Matveyev - One of the best experts on this subject based on the ideXlab platform.

Lih-shyang Chen - One of the best experts on this subject based on the ideXlab platform.

  • Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions.
    The British journal of radiology, 2016
    Co-Authors: Lih-shyang Chen, Ku-yaw Chang, Shao Jer Chen, Ta Wen Hsu, Shu Han Chang, Chih Wen Lin, Yu Ruei Chen, Chin Chiang Hsieh, Shu Chen Han, Chun-ju Hou
    Abstract:

    Objective:In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions.Methods:There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The Marching Cube Algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis.Results:Clinical evaluation confirms GM is significantly superior to...

  • A “Group Marching Cube” (GMC) Algorithm for Speeding up the Marching Cube Algorithm
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Lih-shyang Chen, Young Jinn Lay, Je Bin Huang, Yan De Chen, Ku-yaw Chang, Shao Jer Chen
    Abstract:

    Although the Marching Cube (MC) Algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC Algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) Algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC Algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC Algorithm considers a group of voxels simultaneously for the application of the MC Algorithm to explore interesting features of the original MC Algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC Algorithm is 3 to more than 10 times faster than the Algorithm using a hash table. Another significant advantage of GMC is its compatibility with other Algorithms that accelerate the MC Algorithm. Together, the overall performance of the original MC Algorithm is promoted even further.

  • a group Marching Cube gmc Algorithm for speeding up the Marching Cube Algorithm
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Lih-shyang Chen, Young Jinn Lay, Yan De Chen, Ku-yaw Chang, Je Bin Huang, Shao Jer Chen
    Abstract:

    Although the Marching Cube (MC) Algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC Algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) Algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC Algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC Algorithm considers a group of voxels simultaneously for the application of the MC Algorithm to explore interesting features of the original MC Algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC Algorithm is 3 to more than 10 times faster than the Algorithm using a hash table. Another significant advantage of GMC is its compatibility with other Algorithms that accelerate the MC Algorithm. Together, the overall performance of the original MC Algorithm is promoted even further.

Shao Jer Chen - One of the best experts on this subject based on the ideXlab platform.

  • Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions.
    The British journal of radiology, 2016
    Co-Authors: Lih-shyang Chen, Ku-yaw Chang, Shao Jer Chen, Ta Wen Hsu, Shu Han Chang, Chih Wen Lin, Yu Ruei Chen, Chin Chiang Hsieh, Shu Chen Han, Chun-ju Hou
    Abstract:

    Objective:In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions.Methods:There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The Marching Cube Algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis.Results:Clinical evaluation confirms GM is significantly superior to...

  • A “Group Marching Cube” (GMC) Algorithm for Speeding up the Marching Cube Algorithm
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Lih-shyang Chen, Young Jinn Lay, Je Bin Huang, Yan De Chen, Ku-yaw Chang, Shao Jer Chen
    Abstract:

    Although the Marching Cube (MC) Algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC Algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) Algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC Algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC Algorithm considers a group of voxels simultaneously for the application of the MC Algorithm to explore interesting features of the original MC Algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC Algorithm is 3 to more than 10 times faster than the Algorithm using a hash table. Another significant advantage of GMC is its compatibility with other Algorithms that accelerate the MC Algorithm. Together, the overall performance of the original MC Algorithm is promoted even further.

  • a group Marching Cube gmc Algorithm for speeding up the Marching Cube Algorithm
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Lih-shyang Chen, Young Jinn Lay, Yan De Chen, Ku-yaw Chang, Je Bin Huang, Shao Jer Chen
    Abstract:

    Although the Marching Cube (MC) Algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC Algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) Algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC Algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC Algorithm considers a group of voxels simultaneously for the application of the MC Algorithm to explore interesting features of the original MC Algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC Algorithm is 3 to more than 10 times faster than the Algorithm using a hash table. Another significant advantage of GMC is its compatibility with other Algorithms that accelerate the MC Algorithm. Together, the overall performance of the original MC Algorithm is promoted even further.

Ku-yaw Chang - One of the best experts on this subject based on the ideXlab platform.

  • Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions.
    The British journal of radiology, 2016
    Co-Authors: Lih-shyang Chen, Ku-yaw Chang, Shao Jer Chen, Ta Wen Hsu, Shu Han Chang, Chih Wen Lin, Yu Ruei Chen, Chin Chiang Hsieh, Shu Chen Han, Chun-ju Hou
    Abstract:

    Objective:In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions.Methods:There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The Marching Cube Algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis.Results:Clinical evaluation confirms GM is significantly superior to...

  • A “Group Marching Cube” (GMC) Algorithm for Speeding up the Marching Cube Algorithm
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Lih-shyang Chen, Young Jinn Lay, Je Bin Huang, Yan De Chen, Ku-yaw Chang, Shao Jer Chen
    Abstract:

    Although the Marching Cube (MC) Algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC Algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) Algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC Algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC Algorithm considers a group of voxels simultaneously for the application of the MC Algorithm to explore interesting features of the original MC Algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC Algorithm is 3 to more than 10 times faster than the Algorithm using a hash table. Another significant advantage of GMC is its compatibility with other Algorithms that accelerate the MC Algorithm. Together, the overall performance of the original MC Algorithm is promoted even further.

  • a group Marching Cube gmc Algorithm for speeding up the Marching Cube Algorithm
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Lih-shyang Chen, Young Jinn Lay, Yan De Chen, Ku-yaw Chang, Je Bin Huang, Shao Jer Chen
    Abstract:

    Although the Marching Cube (MC) Algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC Algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) Algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC Algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC Algorithm considers a group of voxels simultaneously for the application of the MC Algorithm to explore interesting features of the original MC Algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC Algorithm is 3 to more than 10 times faster than the Algorithm using a hash table. Another significant advantage of GMC is its compatibility with other Algorithms that accelerate the MC Algorithm. Together, the overall performance of the original MC Algorithm is promoted even further.

Kintomo Takakura - One of the best experts on this subject based on the ideXlab platform.

  • Augmented reality visualization system for intravascular neurosurgery.
    Computer Aided Surgery, 2010
    Co-Authors: Yoshitaka Masutani, Takeyoshi Dohi, Fumitaka Yamane, Hiroshi Iseki, Kintomo Takakura
    Abstract:

    We aimed to construct an augmented reality-based visualization system to support intravascular neurosurgery and evaluate it in clinical environments. Three-dimensional (3D) vascular models are overlaid on motion pictures from X-ray fluoroscopy by 2D/3D registration using fiducial markers. The models are reconstructed from 3D data obtained from X-ray computed tomographic angiography or from magnetic resonance angiography using the Marching-Cube Algorithm. Intraop-erative X-ray images are mapped as texture patterns on a screen object which is displayed with the vascular models. Distortion of X-ray fluoroscopy is eliminated by a new technique of screen mesh deformation. A quantity called reprojection distance was introduced to evaluate the reliability of the displayed images. It predicts the maximum registration error around the registered objects. Analyses of reprojection distances were performed using synthetic data consisting of marker coordinates with 2D or 3D errors. The tolerance of reprojection distan...

  • Biomedical Paper Augmented Reality Visualization System for Intravascular Neurosurgery
    1999
    Co-Authors: Yoshitaka Masutani, Takeyoshi Dohi, Fumitaka Yamane, Hiroshi Iseki, Kintomo Takakura
    Abstract:

    We aimed to construct an augmented reality-based visualization system to support intravascular neurosurgery and evaluate it in clinical environments. Three-dimensional (3D) vascular models are overlaid on motion pictures from X-ray fluoroscopy by 2D/3D registration using fiducial markers. The models are reconstructed from 3D data obtained from X-ray computed tomographic angiography or from magnetic resonance angiography using the Marching-Cube Algorithm. Intraop- erative X-ray images are mapped as texture patterns on a screen object which is displayed with the vascular models. Distortion of X-ray fluoroscopy is eliminated by a new technique of screen mesh deformation. A quantity called reprojection distance was introduced to evaluate the reliability of the displayed images. It predicts the maximum registration error around the registered objects. Analyses of reprojection distances were performed using synthetic data consisting of marker coordinates with 2D or 3D errors. The tolerance of reprojection distance for the clinical environment was determined to be 3.0 mm. The system was tested in two clinical cases in which reprojection distances of 2.6 and 2.09 mm were obtained. Construction and evaluation of our prototype system were successfully carried out. Further development is planned employing a range sensor to permit markerless regis-