Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network
Medical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgmen...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2015/109804 |
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| author | Seiya Tsuda Yuji Iwahori M. K. Bhuyan Robert J. Woodham Kunio Kasugai |
| author_facet | Seiya Tsuda Yuji Iwahori M. K. Bhuyan Robert J. Woodham Kunio Kasugai |
| author_sort | Seiya Tsuda |
| collection | DOAJ |
| description | Medical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgment. As a method to recover 3D shape with high speed, VBW (Vogel-Breuß-Weickert) model is proposed to recover 3D shape under the condition of point light source illumination and perspective projection. However, VBW model recovers the relative shape but there is a problem that the shape cannot be recovered with the exact size. Here, shape modification is introduced to recover the exact shape with modification from that with VBW model. RBF-NN is introduced for the mapping between input and output. Input is given as the output of gradient parameters of VBW model for the generated
sphere. Output is given as the true gradient parameters of true values of the generated sphere. Learning mapping with
NN can modify the gradient and the depth can be recovered according to the modified gradient parameters. Performance of
the proposed approach is confirmed via computer simulation and real experiment. |
| format | Article |
| id | doaj-art-1ac867b249b14ad798f1ddabf08e0ef1 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-1ac867b249b14ad798f1ddabf08e0ef12025-02-03T01:01:52ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/109804109804Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural NetworkSeiya Tsuda0Yuji Iwahori1M. K. Bhuyan2Robert J. Woodham3Kunio Kasugai4Department of Computer Science, Chubu University, 1200 Matsumotocho, Kasugai 487-8501, JapanDepartment of Computer Science, Chubu University, 1200 Matsumotocho, Kasugai 487-8501, JapanDepartment of Electronics and Electrical Engineering, IIT Guwahati, Guwahati 781039, IndiaDepartment of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaDepartment of Gastroenterology, Aichi Medical University, 1-1 Karimata, Yazako, Nagakute 480-1195, JapanMedical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgment. As a method to recover 3D shape with high speed, VBW (Vogel-Breuß-Weickert) model is proposed to recover 3D shape under the condition of point light source illumination and perspective projection. However, VBW model recovers the relative shape but there is a problem that the shape cannot be recovered with the exact size. Here, shape modification is introduced to recover the exact shape with modification from that with VBW model. RBF-NN is introduced for the mapping between input and output. Input is given as the output of gradient parameters of VBW model for the generated sphere. Output is given as the true gradient parameters of true values of the generated sphere. Learning mapping with NN can modify the gradient and the depth can be recovered according to the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment.http://dx.doi.org/10.1155/2015/109804 |
| spellingShingle | Seiya Tsuda Yuji Iwahori M. K. Bhuyan Robert J. Woodham Kunio Kasugai Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network International Journal of Biomedical Imaging |
| title | Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network |
| title_full | Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network |
| title_fullStr | Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network |
| title_full_unstemmed | Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network |
| title_short | Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network |
| title_sort | recovering 3d shape with absolute size from endoscope images using rbf neural network |
| url | http://dx.doi.org/10.1155/2015/109804 |
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