Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding

We introduced a compact representation method named Linear Tensor Coding (LTC) for medical volume. With LTC, medical volumes can be represented by a linear combination of bases which are mutually independent. Furthermore, it is possible to choose the distinctive basis for classification. Before clas...

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Detalles Bibliográficos
Autores principales: Deng, Junping, Qiao, Xu, Chen, Yen-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3708404/
https://www.ncbi.nlm.nih.gov/pubmed/23878617
http://dx.doi.org/10.1155/2013/630902
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author Deng, Junping
Qiao, Xu
Chen, Yen-Wei
author_facet Deng, Junping
Qiao, Xu
Chen, Yen-Wei
author_sort Deng, Junping
collection PubMed
description We introduced a compact representation method named Linear Tensor Coding (LTC) for medical volume. With LTC, medical volumes can be represented by a linear combination of bases which are mutually independent. Furthermore, it is possible to choose the distinctive basis for classification. Before classification, correlations between category labels and the coefficients of LTC basis are used to choose the basis. Then we use the selected basis for classification. The classification accuracy can be significantly improved by the use of selected distinctive basis.
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spelling pubmed-37084042013-07-22 Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding Deng, Junping Qiao, Xu Chen, Yen-Wei Comput Math Methods Med Research Article We introduced a compact representation method named Linear Tensor Coding (LTC) for medical volume. With LTC, medical volumes can be represented by a linear combination of bases which are mutually independent. Furthermore, it is possible to choose the distinctive basis for classification. Before classification, correlations between category labels and the coefficients of LTC basis are used to choose the basis. Then we use the selected basis for classification. The classification accuracy can be significantly improved by the use of selected distinctive basis. Hindawi Publishing Corporation 2013 2013-06-25 /pmc/articles/PMC3708404/ /pubmed/23878617 http://dx.doi.org/10.1155/2013/630902 Text en Copyright © 2013 Junping Deng et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Deng, Junping
Qiao, Xu
Chen, Yen-Wei
Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding
title Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding
title_full Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding
title_fullStr Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding
title_full_unstemmed Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding
title_short Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding
title_sort statistical texture modeling for medical volume using linear tensor coding
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3708404/
https://www.ncbi.nlm.nih.gov/pubmed/23878617
http://dx.doi.org/10.1155/2013/630902
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AT qiaoxu statisticaltexturemodelingformedicalvolumeusinglineartensorcoding
AT chenyenwei statisticaltexturemodelingformedicalvolumeusinglineartensorcoding

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