Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint
Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classificat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193950/ https://www.ncbi.nlm.nih.gov/pubmed/34122001 http://dx.doi.org/10.3389/fnins.2021.679847 |
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author | Gu, Xiaoqing Shen, Zongxuan Xue, Jing Fan, Yiqing Ni, Tongguang |
author_facet | Gu, Xiaoqing Shen, Zongxuan Xue, Jing Fan, Yiqing Ni, Tongguang |
author_sort | Gu, Xiaoqing |
collection | PubMed |
description | Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons. |
format | Online Article Text |
id | pubmed-8193950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81939502021-06-12 Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint Gu, Xiaoqing Shen, Zongxuan Xue, Jing Fan, Yiqing Ni, Tongguang Front Neurosci Neuroscience Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons. Frontiers Media S.A. 2021-05-28 /pmc/articles/PMC8193950/ /pubmed/34122001 http://dx.doi.org/10.3389/fnins.2021.679847 Text en Copyright © 2021 Gu, Shen, Xue, Fan and Ni. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Gu, Xiaoqing Shen, Zongxuan Xue, Jing Fan, Yiqing Ni, Tongguang Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint |
title | Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint |
title_full | Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint |
title_fullStr | Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint |
title_full_unstemmed | Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint |
title_short | Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint |
title_sort | brain tumor mr image classification using convolutional dictionary learning with local constraint |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193950/ https://www.ncbi.nlm.nih.gov/pubmed/34122001 http://dx.doi.org/10.3389/fnins.2021.679847 |
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