The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L’intégrité de la surface des données médicales 3D est cruciale pour la simulation chirurgicale ou les diagnostics virtuels. Cependant, des trous indésirables existent souvent en raison de dommages externes sur les corps ou d'une limitation d'accessibilité sur les scanners. Pour combler ce fossé, le remplissage des trous pour l'imagerie médicale est un sujet de recherche populaire ces dernières années [1]-[3]. Considérant qu'une image médicale, par exemple CT ou IRM, a la forme naturelle d'un tenseur, nous reconnaissons le problème du remplissage médical des trous comme l'extension du problème de poursuite en composantes principales (PCP) du cas matriciel au cas tensoriel. Puisque le nouveau problème dans le cas tensoriel est beaucoup plus difficile que dans le cas matriciel, un algorithme efficace pour l’extension est présenté par technique de relaxation. La caractéristique la plus significative de notre algorithme est que contrairement aux méthodes traditionnelles qui suivent une approche strictement locale, notre méthode comble le trou par la structure globale des données médicales spécifiques. Une autre différence importante par rapport à l’algorithme précédent [4] est que notre algorithme est capable de séparer automatiquement les données complétées du trou de manière implicite. Nos expériences démontrent que la méthode proposée peut conduire à des résultats satisfaisants.
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Lv GUO, Yin LI, Jie YANG, Li LU, "Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 2, pp. 396-399, February 2011, doi: 10.1587/transinf.E94.D.396.
Abstract: Surface integrity of 3D medical data is crucial for surgery simulation or virtual diagnoses. However, undesirable holes often exist due to external damage on bodies or accessibility limitation on scanners. To bridge the gap, hole-filling for medical imaging is a popular research topic in recent years [1]-[3]. Considering that a medical image, e.g. CT or MRI, has the natural form of a tensor, we recognize the problem of medical hole-filling as the extension of Principal Component Pursuit (PCP) problem from matrix case to tensor case. Since the new problem in the tensor case is much more difficult than the matrix case, an efficient algorithm for the extension is presented by relaxation technique. The most significant feature of our algorithm is that unlike traditional methods which follow a strictly local approach, our method fixes the hole by the global structure in the specific medical data. Another important difference from the previous algorithm [4] is that our algorithm is able to automatically separate the completed data from the hole in an implicit manner. Our experiments demonstrate that the proposed method can lead to satisfactory results.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.396/_p
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@ARTICLE{e94-d_2_396,
author={Lv GUO, Yin LI, Jie YANG, Li LU, },
journal={IEICE TRANSACTIONS on Information},
title={Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging},
year={2011},
volume={E94-D},
number={2},
pages={396-399},
abstract={Surface integrity of 3D medical data is crucial for surgery simulation or virtual diagnoses. However, undesirable holes often exist due to external damage on bodies or accessibility limitation on scanners. To bridge the gap, hole-filling for medical imaging is a popular research topic in recent years [1]-[3]. Considering that a medical image, e.g. CT or MRI, has the natural form of a tensor, we recognize the problem of medical hole-filling as the extension of Principal Component Pursuit (PCP) problem from matrix case to tensor case. Since the new problem in the tensor case is much more difficult than the matrix case, an efficient algorithm for the extension is presented by relaxation technique. The most significant feature of our algorithm is that unlike traditional methods which follow a strictly local approach, our method fixes the hole by the global structure in the specific medical data. Another important difference from the previous algorithm [4] is that our algorithm is able to automatically separate the completed data from the hole in an implicit manner. Our experiments demonstrate that the proposed method can lead to satisfactory results.},
keywords={},
doi={10.1587/transinf.E94.D.396},
ISSN={1745-1361},
month={February},}
Copier
TY - JOUR
TI - Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging
T2 - IEICE TRANSACTIONS on Information
SP - 396
EP - 399
AU - Lv GUO
AU - Yin LI
AU - Jie YANG
AU - Li LU
PY - 2011
DO - 10.1587/transinf.E94.D.396
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2011
AB - Surface integrity of 3D medical data is crucial for surgery simulation or virtual diagnoses. However, undesirable holes often exist due to external damage on bodies or accessibility limitation on scanners. To bridge the gap, hole-filling for medical imaging is a popular research topic in recent years [1]-[3]. Considering that a medical image, e.g. CT or MRI, has the natural form of a tensor, we recognize the problem of medical hole-filling as the extension of Principal Component Pursuit (PCP) problem from matrix case to tensor case. Since the new problem in the tensor case is much more difficult than the matrix case, an efficient algorithm for the extension is presented by relaxation technique. The most significant feature of our algorithm is that unlike traditional methods which follow a strictly local approach, our method fixes the hole by the global structure in the specific medical data. Another important difference from the previous algorithm [4] is that our algorithm is able to automatically separate the completed data from the hole in an implicit manner. Our experiments demonstrate that the proposed method can lead to satisfactory results.
ER -