Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method

Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is conside...
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Gespeichert in:

Autor*in:	Prasun Chandra Tripathi [verfasserIn] Soumen Bag [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images

Übergeordnetes Werk:	In: IET Image Processing - Wiley, 2021, 14(2020), 15, Seite 3705-3717
Übergeordnetes Werk:	volume:14 ; year:2020 ; number:15 ; pages:3705-3717

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1049/iet-ipr.2020.0383

Katalog-ID:	DOAJ036484989

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allfields	10.1049/iet-ipr.2020.0383 doi (DE-627)DOAJ036484989 (DE-599)DOAJ7fee9c8c6aeb4824b52d6f0f3a6eb09b DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Prasun Chandra Tripathi verfasserin aut Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations. brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images Photography Computer software Soumen Bag verfasserin aut In IET Image Processing Wiley, 2021 14(2020), 15, Seite 3705-3717 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:14 year:2020 number:15 pages:3705-3717 https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/article/7fee9c8c6aeb4824b52d6f0f3a6eb09b kostenfrei https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 15 3705-3717
spelling	10.1049/iet-ipr.2020.0383 doi (DE-627)DOAJ036484989 (DE-599)DOAJ7fee9c8c6aeb4824b52d6f0f3a6eb09b DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Prasun Chandra Tripathi verfasserin aut Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations. brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images Photography Computer software Soumen Bag verfasserin aut In IET Image Processing Wiley, 2021 14(2020), 15, Seite 3705-3717 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:14 year:2020 number:15 pages:3705-3717 https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/article/7fee9c8c6aeb4824b52d6f0f3a6eb09b kostenfrei https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 15 3705-3717
allfields_unstemmed	10.1049/iet-ipr.2020.0383 doi (DE-627)DOAJ036484989 (DE-599)DOAJ7fee9c8c6aeb4824b52d6f0f3a6eb09b DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Prasun Chandra Tripathi verfasserin aut Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations. brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images Photography Computer software Soumen Bag verfasserin aut In IET Image Processing Wiley, 2021 14(2020), 15, Seite 3705-3717 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:14 year:2020 number:15 pages:3705-3717 https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/article/7fee9c8c6aeb4824b52d6f0f3a6eb09b kostenfrei https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 15 3705-3717
allfieldsGer	10.1049/iet-ipr.2020.0383 doi (DE-627)DOAJ036484989 (DE-599)DOAJ7fee9c8c6aeb4824b52d6f0f3a6eb09b DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Prasun Chandra Tripathi verfasserin aut Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations. brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images Photography Computer software Soumen Bag verfasserin aut In IET Image Processing Wiley, 2021 14(2020), 15, Seite 3705-3717 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:14 year:2020 number:15 pages:3705-3717 https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/article/7fee9c8c6aeb4824b52d6f0f3a6eb09b kostenfrei https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 15 3705-3717
allfieldsSound	10.1049/iet-ipr.2020.0383 doi (DE-627)DOAJ036484989 (DE-599)DOAJ7fee9c8c6aeb4824b52d6f0f3a6eb09b DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Prasun Chandra Tripathi verfasserin aut Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations. brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images Photography Computer software Soumen Bag verfasserin aut In IET Image Processing Wiley, 2021 14(2020), 15, Seite 3705-3717 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:14 year:2020 number:15 pages:3705-3717 https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/article/7fee9c8c6aeb4824b52d6f0f3a6eb09b kostenfrei https://doi.org/10.1049/iet-ipr.2020.0383 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 15 3705-3717
language	English
source	In IET Image Processing 14(2020), 15, Seite 3705-3717 volume:14 year:2020 number:15 pages:3705-3717
sourceStr	In IET Image Processing 14(2020), 15, Seite 3705-3717 volume:14 year:2020 number:15 pages:3705-3717
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images Photography Computer software
isfreeaccess_bool	true
container_title	IET Image Processing
authorswithroles_txt_mv	Prasun Chandra Tripathi @@aut@@ Soumen Bag @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	527265993
id	DOAJ036484989
language_de	englisch
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callnumber-first	T - Technology
author	Prasun Chandra Tripathi
spellingShingle	Prasun Chandra Tripathi misc TR1-1050 misc QA76.75-76.765 misc brain magnetic resonance images misc computer‐aided diagnosis misc brain diseases misc brain MR images misc LIFCM misc MR brain images misc Photography misc Computer software Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method
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topic_title	TR1-1050 QA76.75-76.765 Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images
topic	misc TR1-1050 misc QA76.75-76.765 misc brain magnetic resonance images misc computer‐aided diagnosis misc brain diseases misc brain MR images misc LIFCM misc MR brain images misc Photography misc Computer software
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title	Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method
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title_sort	segmentation of brain magnetic resonance images using a novel fuzzy clustering based method
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title_auth	Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method
abstract	Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.
abstractGer	Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.
abstract_unstemmed	Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.
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title_short	Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method
url	https://doi.org/10.1049/iet-ipr.2020.0383 https://doaj.org/article/7fee9c8c6aeb4824b52d6f0f3a6eb09b https://doaj.org/toc/1751-9659 https://doaj.org/toc/1751-9667
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up_date	2024-07-03T20:50:27.837Z
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Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method

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