Medical image classification based on multi-scale non-negative sparse coding
•We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms.•We utilize multi-scale decomposition method to decompose images into multiple scale layers and extract more representative image features.•We introduce fis...
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| Vydané v: | Artificial intelligence in medicine Ročník 83; s. 44 - 51 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.11.2017
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| ISSN: | 0933-3657, 1873-2860, 1873-2860 |
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| Abstract | •We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms.•We utilize multi-scale decomposition method to decompose images into multiple scale layers and extract more representative image features.•We introduce fisher discriminative analysis algorithm to non-negative sparse coding model thus to exploit more contextual spatial information.•Our model performs superior to other related algorithms in terms of efficiency and classification accuracy.
With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. |
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| AbstractList | With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. •We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms.•We utilize multi-scale decomposition method to decompose images into multiple scale layers and extract more representative image features.•We introduce fisher discriminative analysis algorithm to non-negative sparse coding model thus to exploit more contextual spatial information.•Our model performs superior to other related algorithms in terms of efficiency and classification accuracy. With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Highlights • We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms. • We utilize multi-scale decomposition method to decompose images into multiple scale layers and extract more representative image features. • We introduce fisher discriminative analysis algorithm to non-negative sparse coding model thus to exploit more contextual spatial information. • Our model performs superior to other related algorithms in terms of efficiency and classification accuracy. With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. |
| Author | Wei, Fushan Shen, Jian Zhang, Ruijie Li, Xiong Sangaiah, Arun Kumar |
| Author_xml | – sequence: 1 givenname: Ruijie surname: Zhang fullname: Zhang, Ruijie email: rjz_wonder@163.com organization: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China – sequence: 2 givenname: Jian surname: Shen fullname: Shen, Jian organization: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China – sequence: 3 givenname: Fushan surname: Wei fullname: Wei, Fushan email: weifs831020@163.com organization: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China – sequence: 4 givenname: Xiong surname: Li fullname: Li, Xiong email: lixiongzhq@163.com organization: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China – sequence: 5 givenname: Arun Kumar surname: Sangaiah fullname: Sangaiah, Arun Kumar email: arunkumarsangaiah@gmail.com organization: School of Computer Science and Engineering, VIT University, Vellore, 632014, Tamilnadu, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28559133$$D View this record in MEDLINE/PubMed |
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| Keywords | The semantic gap Multi-scale decomposition Non-negative sparse coding Fisher discriminative analysis Medical image classification |
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| Snippet | •We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms.•We utilize... Highlights • We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms. •... With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and... |
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| SubjectTerms | Artificial Intelligence Diagnostic Imaging - classification Fisher discriminative analysis Humans Image Interpretation, Computer-Assisted - methods Internal Medicine Medical image classification Multi-scale decomposition Non-negative sparse coding Other Pattern Recognition, Automated Predictive Value of Tests ROC Curve The semantic gap |
| Title | Medical image classification based on multi-scale non-negative sparse coding |
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