GPU-Accelerated Features Extraction From Magnetic Resonance Images

The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method...
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Autor*in:	Hsin-Yi Tsai [verfasserIn] Hanyu Zhang [verfasserIn] Che-Lun Hung [verfasserIn] Geyong Min [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Magnetic resonance imaging (MRI) gray-level co-occurrence matrix (GLCM) texture features extraction GPGPU image analysis computer science

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 5(2017), Seite 22634-22646
Übergeordnetes Werk:	volume:5 ; year:2017 ; pages:22634-22646

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2017.2756624

Katalog-ID:	DOAJ051939959

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spelling	10.1109/ACCESS.2017.2756624 doi (DE-627)DOAJ051939959 (DE-599)DOAJe5ea3bd52aef4bfb8c98a6e73a382c5e DE-627 ger DE-627 rakwb eng TK1-9971 Hsin-Yi Tsai verfasserin aut GPU-Accelerated Features Extraction From Magnetic Resonance Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method to parallelize extraction of a set of features based on the gray-level co-occurrence matrix (GLCM), which may be the most widely, used method. The method is evaluated on various GPU devices and compared with its serial counterpart implemented and optimized in both Matlab and C on a single machine. A series of experimental tests focused on magnetic resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to its serial counterpart, as it could achieve more than 25-105 folds of speedup for single precision and more than 15-85 folds of speedup for double precision on Geforce GTX 1080 along different size of ROIs. Magnetic resonance imaging (MRI) gray-level co-occurrence matrix (GLCM) texture features extraction GPGPU image analysis computer science Electrical engineering. Electronics. Nuclear engineering Hanyu Zhang verfasserin aut Che-Lun Hung verfasserin aut Geyong Min verfasserin aut In IEEE Access IEEE, 2014 5(2017), Seite 22634-22646 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:5 year:2017 pages:22634-22646 https://doi.org/10.1109/ACCESS.2017.2756624 kostenfrei https://doaj.org/article/e5ea3bd52aef4bfb8c98a6e73a382c5e kostenfrei https://ieeexplore.ieee.org/document/8049449/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 22634-22646
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allfieldsSound	10.1109/ACCESS.2017.2756624 doi (DE-627)DOAJ051939959 (DE-599)DOAJe5ea3bd52aef4bfb8c98a6e73a382c5e DE-627 ger DE-627 rakwb eng TK1-9971 Hsin-Yi Tsai verfasserin aut GPU-Accelerated Features Extraction From Magnetic Resonance Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method to parallelize extraction of a set of features based on the gray-level co-occurrence matrix (GLCM), which may be the most widely, used method. The method is evaluated on various GPU devices and compared with its serial counterpart implemented and optimized in both Matlab and C on a single machine. A series of experimental tests focused on magnetic resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to its serial counterpart, as it could achieve more than 25-105 folds of speedup for single precision and more than 15-85 folds of speedup for double precision on Geforce GTX 1080 along different size of ROIs. Magnetic resonance imaging (MRI) gray-level co-occurrence matrix (GLCM) texture features extraction GPGPU image analysis computer science Electrical engineering. Electronics. Nuclear engineering Hanyu Zhang verfasserin aut Che-Lun Hung verfasserin aut Geyong Min verfasserin aut In IEEE Access IEEE, 2014 5(2017), Seite 22634-22646 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:5 year:2017 pages:22634-22646 https://doi.org/10.1109/ACCESS.2017.2756624 kostenfrei https://doaj.org/article/e5ea3bd52aef4bfb8c98a6e73a382c5e kostenfrei https://ieeexplore.ieee.org/document/8049449/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 22634-22646
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callnumber-first	T - Technology
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spellingShingle	Hsin-Yi Tsai misc TK1-9971 misc Magnetic resonance imaging (MRI) misc gray-level co-occurrence matrix (GLCM) misc texture features extraction misc GPGPU misc image analysis misc computer science misc Electrical engineering. Electronics. Nuclear engineering GPU-Accelerated Features Extraction From Magnetic Resonance Images
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topic_title	TK1-9971 GPU-Accelerated Features Extraction From Magnetic Resonance Images Magnetic resonance imaging (MRI) gray-level co-occurrence matrix (GLCM) texture features extraction GPGPU image analysis computer science
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topic_unstemmed	misc TK1-9971 misc Magnetic resonance imaging (MRI) misc gray-level co-occurrence matrix (GLCM) misc texture features extraction misc GPGPU misc image analysis misc computer science misc Electrical engineering. Electronics. Nuclear engineering
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title	GPU-Accelerated Features Extraction From Magnetic Resonance Images
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title_auth	GPU-Accelerated Features Extraction From Magnetic Resonance Images
abstract	The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method to parallelize extraction of a set of features based on the gray-level co-occurrence matrix (GLCM), which may be the most widely, used method. The method is evaluated on various GPU devices and compared with its serial counterpart implemented and optimized in both Matlab and C on a single machine. A series of experimental tests focused on magnetic resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to its serial counterpart, as it could achieve more than 25-105 folds of speedup for single precision and more than 15-85 folds of speedup for double precision on Geforce GTX 1080 along different size of ROIs.
abstractGer	The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method to parallelize extraction of a set of features based on the gray-level co-occurrence matrix (GLCM), which may be the most widely, used method. The method is evaluated on various GPU devices and compared with its serial counterpart implemented and optimized in both Matlab and C on a single machine. A series of experimental tests focused on magnetic resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to its serial counterpart, as it could achieve more than 25-105 folds of speedup for single precision and more than 15-85 folds of speedup for double precision on Geforce GTX 1080 along different size of ROIs.
abstract_unstemmed	The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method to parallelize extraction of a set of features based on the gray-level co-occurrence matrix (GLCM), which may be the most widely, used method. The method is evaluated on various GPU devices and compared with its serial counterpart implemented and optimized in both Matlab and C on a single machine. A series of experimental tests focused on magnetic resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to its serial counterpart, as it could achieve more than 25-105 folds of speedup for single precision and more than 15-85 folds of speedup for double precision on Geforce GTX 1080 along different size of ROIs.
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title_short	GPU-Accelerated Features Extraction From Magnetic Resonance Images
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