Medical image segmentation: hard and soft computing approaches

Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard c...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sinha, Prajawal [verfasserIn] Tuteja, Mayur Saxena, Sanjay

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Medical image segmentation Hard computing Soft computing Medical imaging

Anmerkung:	© Springer Nature Switzerland AG 2020

Übergeordnetes Werk:	Enthalten in: SN applied sciences - [Cham] : Springer International Publishing, 2019, 2(2020), 2 vom: 07. Jan.
Übergeordnetes Werk:	volume:2 ; year:2020 ; number:2 ; day:07 ; month:01

Links:	Volltext

DOI / URN:	10.1007/s42452-020-1956-4

Katalog-ID:	SPR038582724

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520			\|a Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation.
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publishDate	2020
allfields	10.1007/s42452-020-1956-4 doi (DE-627)SPR038582724 (SPR)s42452-020-1956-4-e DE-627 ger DE-627 rakwb eng Sinha, Prajawal verfasserin aut Medical image segmentation: hard and soft computing approaches 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2020 Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. Medical image segmentation (dpeaa)DE-He213 Hard computing (dpeaa)DE-He213 Soft computing (dpeaa)DE-He213 Medical imaging (dpeaa)DE-He213 Tuteja, Mayur aut Saxena, Sanjay aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2020), 2 vom: 07. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2020 number:2 day:07 month:01 https://dx.doi.org/10.1007/s42452-020-1956-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 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_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 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 2 2020 2 07 01
spelling	10.1007/s42452-020-1956-4 doi (DE-627)SPR038582724 (SPR)s42452-020-1956-4-e DE-627 ger DE-627 rakwb eng Sinha, Prajawal verfasserin aut Medical image segmentation: hard and soft computing approaches 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2020 Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. Medical image segmentation (dpeaa)DE-He213 Hard computing (dpeaa)DE-He213 Soft computing (dpeaa)DE-He213 Medical imaging (dpeaa)DE-He213 Tuteja, Mayur aut Saxena, Sanjay aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2020), 2 vom: 07. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2020 number:2 day:07 month:01 https://dx.doi.org/10.1007/s42452-020-1956-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 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_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 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 2 2020 2 07 01
allfields_unstemmed	10.1007/s42452-020-1956-4 doi (DE-627)SPR038582724 (SPR)s42452-020-1956-4-e DE-627 ger DE-627 rakwb eng Sinha, Prajawal verfasserin aut Medical image segmentation: hard and soft computing approaches 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2020 Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. Medical image segmentation (dpeaa)DE-He213 Hard computing (dpeaa)DE-He213 Soft computing (dpeaa)DE-He213 Medical imaging (dpeaa)DE-He213 Tuteja, Mayur aut Saxena, Sanjay aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2020), 2 vom: 07. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2020 number:2 day:07 month:01 https://dx.doi.org/10.1007/s42452-020-1956-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 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_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 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 2 2020 2 07 01
allfieldsGer	10.1007/s42452-020-1956-4 doi (DE-627)SPR038582724 (SPR)s42452-020-1956-4-e DE-627 ger DE-627 rakwb eng Sinha, Prajawal verfasserin aut Medical image segmentation: hard and soft computing approaches 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2020 Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. Medical image segmentation (dpeaa)DE-He213 Hard computing (dpeaa)DE-He213 Soft computing (dpeaa)DE-He213 Medical imaging (dpeaa)DE-He213 Tuteja, Mayur aut Saxena, Sanjay aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2020), 2 vom: 07. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2020 number:2 day:07 month:01 https://dx.doi.org/10.1007/s42452-020-1956-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 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_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 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 2 2020 2 07 01
allfieldsSound	10.1007/s42452-020-1956-4 doi (DE-627)SPR038582724 (SPR)s42452-020-1956-4-e DE-627 ger DE-627 rakwb eng Sinha, Prajawal verfasserin aut Medical image segmentation: hard and soft computing approaches 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2020 Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. Medical image segmentation (dpeaa)DE-He213 Hard computing (dpeaa)DE-He213 Soft computing (dpeaa)DE-He213 Medical imaging (dpeaa)DE-He213 Tuteja, Mayur aut Saxena, Sanjay aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2020), 2 vom: 07. Jan. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2020 number:2 day:07 month:01 https://dx.doi.org/10.1007/s42452-020-1956-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 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_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 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 2 2020 2 07 01
language	English
source	Enthalten in SN applied sciences 2(2020), 2 vom: 07. Jan. volume:2 year:2020 number:2 day:07 month:01
sourceStr	Enthalten in SN applied sciences 2(2020), 2 vom: 07. Jan. volume:2 year:2020 number:2 day:07 month:01
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Medical image segmentation Hard computing Soft computing Medical imaging
isfreeaccess_bool	false
container_title	SN applied sciences
authorswithroles_txt_mv	Sinha, Prajawal @@aut@@ Tuteja, Mayur @@aut@@ Saxena, Sanjay @@aut@@
publishDateDaySort_date	2020-01-07T00:00:00Z
hierarchy_top_id	103761139X
id	SPR038582724
language_de	englisch
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author	Sinha, Prajawal
spellingShingle	Sinha, Prajawal misc Medical image segmentation misc Hard computing misc Soft computing misc Medical imaging Medical image segmentation: hard and soft computing approaches
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topic_title	Medical image segmentation: hard and soft computing approaches Medical image segmentation (dpeaa)DE-He213 Hard computing (dpeaa)DE-He213 Soft computing (dpeaa)DE-He213 Medical imaging (dpeaa)DE-He213
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title	Medical image segmentation: hard and soft computing approaches
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title_full	Medical image segmentation: hard and soft computing approaches
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title_sort	medical image segmentation: hard and soft computing approaches
title_auth	Medical image segmentation: hard and soft computing approaches
abstract	Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. © Springer Nature Switzerland AG 2020
abstractGer	Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. © Springer Nature Switzerland AG 2020
abstract_unstemmed	Abstract Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation. © Springer Nature Switzerland AG 2020
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title_short	Medical image segmentation: hard and soft computing approaches
url	https://dx.doi.org/10.1007/s42452-020-1956-4
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To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. 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Medical image segmentation: hard and soft computing approaches

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