Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation

Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral fi...
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Autor*in:	Xiao, Leyi [verfasserIn] Fan, Chaodong Ouyang, Honglin Abate, Andrea F. Wan, Shaohua

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Image segmentation Otsu method Adaptive filtering Hierarchical threshold Brain MR image

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 13(2021), 4 vom: 01. März, Seite 2161-2176
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:4 ; day:01 ; month:03 ; pages:2161-2176

Links:	Volltext

DOI / URN:	10.1007/s12652-021-02976-6

Katalog-ID:	SPR046596399

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520			\|a Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation.
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allfields	10.1007/s12652-021-02976-6 doi (DE-627)SPR046596399 (SPR)s12652-021-02976-6-e DE-627 ger DE-627 rakwb eng Xiao, Leyi verfasserin aut Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. Image segmentation (dpeaa)DE-He213 Otsu method (dpeaa)DE-He213 Adaptive filtering (dpeaa)DE-He213 Hierarchical threshold (dpeaa)DE-He213 Brain MR image (dpeaa)DE-He213 Fan, Chaodong aut Ouyang, Honglin aut Abate, Andrea F. aut Wan, Shaohua (orcid)0000-0001-7013-9081 aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 13(2021), 4 vom: 01. März, Seite 2161-2176 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176 https://dx.doi.org/10.1007/s12652-021-02976-6 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_74 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_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 13 2021 4 01 03 2161-2176
spelling	10.1007/s12652-021-02976-6 doi (DE-627)SPR046596399 (SPR)s12652-021-02976-6-e DE-627 ger DE-627 rakwb eng Xiao, Leyi verfasserin aut Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. Image segmentation (dpeaa)DE-He213 Otsu method (dpeaa)DE-He213 Adaptive filtering (dpeaa)DE-He213 Hierarchical threshold (dpeaa)DE-He213 Brain MR image (dpeaa)DE-He213 Fan, Chaodong aut Ouyang, Honglin aut Abate, Andrea F. aut Wan, Shaohua (orcid)0000-0001-7013-9081 aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 13(2021), 4 vom: 01. März, Seite 2161-2176 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176 https://dx.doi.org/10.1007/s12652-021-02976-6 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_74 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_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 13 2021 4 01 03 2161-2176
allfields_unstemmed	10.1007/s12652-021-02976-6 doi (DE-627)SPR046596399 (SPR)s12652-021-02976-6-e DE-627 ger DE-627 rakwb eng Xiao, Leyi verfasserin aut Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. Image segmentation (dpeaa)DE-He213 Otsu method (dpeaa)DE-He213 Adaptive filtering (dpeaa)DE-He213 Hierarchical threshold (dpeaa)DE-He213 Brain MR image (dpeaa)DE-He213 Fan, Chaodong aut Ouyang, Honglin aut Abate, Andrea F. aut Wan, Shaohua (orcid)0000-0001-7013-9081 aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 13(2021), 4 vom: 01. März, Seite 2161-2176 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176 https://dx.doi.org/10.1007/s12652-021-02976-6 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_74 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_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 13 2021 4 01 03 2161-2176
allfieldsGer	10.1007/s12652-021-02976-6 doi (DE-627)SPR046596399 (SPR)s12652-021-02976-6-e DE-627 ger DE-627 rakwb eng Xiao, Leyi verfasserin aut Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. Image segmentation (dpeaa)DE-He213 Otsu method (dpeaa)DE-He213 Adaptive filtering (dpeaa)DE-He213 Hierarchical threshold (dpeaa)DE-He213 Brain MR image (dpeaa)DE-He213 Fan, Chaodong aut Ouyang, Honglin aut Abate, Andrea F. aut Wan, Shaohua (orcid)0000-0001-7013-9081 aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 13(2021), 4 vom: 01. März, Seite 2161-2176 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176 https://dx.doi.org/10.1007/s12652-021-02976-6 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_74 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_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 13 2021 4 01 03 2161-2176
allfieldsSound	10.1007/s12652-021-02976-6 doi (DE-627)SPR046596399 (SPR)s12652-021-02976-6-e DE-627 ger DE-627 rakwb eng Xiao, Leyi verfasserin aut Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. Image segmentation (dpeaa)DE-He213 Otsu method (dpeaa)DE-He213 Adaptive filtering (dpeaa)DE-He213 Hierarchical threshold (dpeaa)DE-He213 Brain MR image (dpeaa)DE-He213 Fan, Chaodong aut Ouyang, Honglin aut Abate, Andrea F. aut Wan, Shaohua (orcid)0000-0001-7013-9081 aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 13(2021), 4 vom: 01. März, Seite 2161-2176 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176 https://dx.doi.org/10.1007/s12652-021-02976-6 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_74 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_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 13 2021 4 01 03 2161-2176
language	English
source	Enthalten in Journal of ambient intelligence and humanized computing 13(2021), 4 vom: 01. März, Seite 2161-2176 volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176
sourceStr	Enthalten in Journal of ambient intelligence and humanized computing 13(2021), 4 vom: 01. März, Seite 2161-2176 volume:13 year:2021 number:4 day:01 month:03 pages:2161-2176
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image segmentation Otsu method Adaptive filtering Hierarchical threshold Brain MR image
isfreeaccess_bool	false
container_title	Journal of ambient intelligence and humanized computing
authorswithroles_txt_mv	Xiao, Leyi @@aut@@ Fan, Chaodong @@aut@@ Ouyang, Honglin @@aut@@ Abate, Andrea F. @@aut@@ Wan, Shaohua @@aut@@
publishDateDaySort_date	2021-03-01T00:00:00Z
hierarchy_top_id	620775734
id	SPR046596399
language_de	englisch
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author	Xiao, Leyi
spellingShingle	Xiao, Leyi misc Image segmentation misc Otsu method misc Adaptive filtering misc Hierarchical threshold misc Brain MR image Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation
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topic_title	Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation Image segmentation (dpeaa)DE-He213 Otsu method (dpeaa)DE-He213 Adaptive filtering (dpeaa)DE-He213 Hierarchical threshold (dpeaa)DE-He213 Brain MR image (dpeaa)DE-He213
topic	misc Image segmentation misc Otsu method misc Adaptive filtering misc Hierarchical threshold misc Brain MR image
topic_unstemmed	misc Image segmentation misc Otsu method misc Adaptive filtering misc Hierarchical threshold misc Brain MR image
topic_browse	misc Image segmentation misc Otsu method misc Adaptive filtering misc Hierarchical threshold misc Brain MR image
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title	Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation
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title_full	Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation
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author_browse	Xiao, Leyi Fan, Chaodong Ouyang, Honglin Abate, Andrea F. Wan, Shaohua
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title_sort	adaptive trapezoid region intercept histogram based otsu method for brain mr image segmentation
title_auth	Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation
abstract	Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
abstractGer	Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
abstract_unstemmed	Abstract In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
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title_short	Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation
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Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation

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