HS–GS: A Method for Multicenter MR Image Standardization

The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, makin...
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Gespeichert in:

Autor*in:	Guohua Zhao [verfasserIn] Jie Bai [verfasserIn] Pei Pei Wang [verfasserIn] Guan Yang [verfasserIn] Lei Shi [verfasserIn] Yongcai Tao [verfasserIn] Yusong Lin [verfasserIn] Jingliang Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Data standardization histogram specification grid search MRI radiomics

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 158512-158522
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:158512-158522

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3020369

Katalog-ID:	DOAJ072587881

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spelling	10.1109/ACCESS.2020.3020369 doi (DE-627)DOAJ072587881 (DE-599)DOAJ9913302438f64f5e9f1f84c7ab497cbe DE-627 ger DE-627 rakwb eng TK1-9971 Guohua Zhao verfasserin aut HS–GS: A Method for Multicenter MR Image Standardization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems. Data standardization histogram specification grid search MRI radiomics Electrical engineering. Electronics. Nuclear engineering Jie Bai verfasserin aut Pei Pei Wang verfasserin aut Guan Yang verfasserin aut Lei Shi verfasserin aut Yongcai Tao verfasserin aut Yusong Lin verfasserin aut Jingliang Cheng verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 158512-158522 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:158512-158522 https://doi.org/10.1109/ACCESS.2020.3020369 kostenfrei https://doaj.org/article/9913302438f64f5e9f1f84c7ab497cbe kostenfrei https://ieeexplore.ieee.org/document/9180339/ 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 8 2020 158512-158522
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allfieldsSound	10.1109/ACCESS.2020.3020369 doi (DE-627)DOAJ072587881 (DE-599)DOAJ9913302438f64f5e9f1f84c7ab497cbe DE-627 ger DE-627 rakwb eng TK1-9971 Guohua Zhao verfasserin aut HS–GS: A Method for Multicenter MR Image Standardization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems. Data standardization histogram specification grid search MRI radiomics Electrical engineering. Electronics. Nuclear engineering Jie Bai verfasserin aut Pei Pei Wang verfasserin aut Guan Yang verfasserin aut Lei Shi verfasserin aut Yongcai Tao verfasserin aut Yusong Lin verfasserin aut Jingliang Cheng verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 158512-158522 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:158512-158522 https://doi.org/10.1109/ACCESS.2020.3020369 kostenfrei https://doaj.org/article/9913302438f64f5e9f1f84c7ab497cbe kostenfrei https://ieeexplore.ieee.org/document/9180339/ 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 8 2020 158512-158522
language	English
source	In IEEE Access 8(2020), Seite 158512-158522 volume:8 year:2020 pages:158512-158522
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institution	findex.gbv.de
topic_facet	Data standardization histogram specification grid search MRI radiomics Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Guohua Zhao @@aut@@ Jie Bai @@aut@@ Pei Pei Wang @@aut@@ Guan Yang @@aut@@ Lei Shi @@aut@@ Yongcai Tao @@aut@@ Yusong Lin @@aut@@ Jingliang Cheng @@aut@@
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callnumber-first	T - Technology
author	Guohua Zhao
spellingShingle	Guohua Zhao misc TK1-9971 misc Data standardization misc histogram specification misc grid search misc MRI misc radiomics misc Electrical engineering. Electronics. Nuclear engineering HS–GS: A Method for Multicenter MR Image Standardization
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topic_title	TK1-9971 HS–GS: A Method for Multicenter MR Image Standardization Data standardization histogram specification grid search MRI radiomics
topic	misc TK1-9971 misc Data standardization misc histogram specification misc grid search misc MRI misc radiomics misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Data standardization misc histogram specification misc grid search misc MRI misc radiomics misc Electrical engineering. Electronics. Nuclear engineering
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title	HS–GS: A Method for Multicenter MR Image Standardization
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title_full	HS–GS: A Method for Multicenter MR Image Standardization
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title_sort	hs–gs: a method for multicenter mr image standardization
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title_auth	HS–GS: A Method for Multicenter MR Image Standardization
abstract	The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems.
abstractGer	The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems.
abstract_unstemmed	The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems.
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title_short	HS–GS: A Method for Multicenter MR Image Standardization
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