Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation
Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jia, Heming [verfasserIn] Peng, Xiaoxu [verfasserIn] Kang, Lifei [verfasserIn] Li, Yao [verfasserIn] Jiang, Zichao [verfasserIn] Sun, Kangjian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 |
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| Übergeordnetes Werk: |
volume:79 ; year:2020 ; number:37-38 ; day:02 ; month:08 ; pages:28369-28392 |
| Links: |
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| DOI / URN: |
10.1007/s11042-020-09228-3 |
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| Katalog-ID: |
SPR041192788 |
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| 245 | 1 | 0 | |a Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation |
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| 520 | |a Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. | ||
| 650 | 4 | |a Image segmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pulse coupled neural network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Harris hawks optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mutual information entropy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image entropy |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Peng, Xiaoxu |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Lifei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Zichao |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Kangjian |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 |g 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 |w (DE-627)27135030X |w (DE-600)1479928-5 |x 1573-7721 |7 nnns |
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10.1007/s11042-020-09228-3 doi (DE-627)SPR041192788 (SPR)s11042-020-09228-3-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Jia, Heming verfasserin aut Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. Image segmentation (dpeaa)DE-He213 Pulse coupled neural network (dpeaa)DE-He213 Harris hawks optimization (dpeaa)DE-He213 Mutual information entropy (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Peng, Xiaoxu verfasserin aut Kang, Lifei verfasserin aut Li, Yao verfasserin aut Jiang, Zichao verfasserin aut Sun, Kangjian verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:37-38 day:02 month:08 pages:28369-28392 https://dx.doi.org/10.1007/s11042-020-09228-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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 54.87 ASE AR 79 2020 37-38 02 08 28369-28392 |
| spelling |
10.1007/s11042-020-09228-3 doi (DE-627)SPR041192788 (SPR)s11042-020-09228-3-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Jia, Heming verfasserin aut Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. Image segmentation (dpeaa)DE-He213 Pulse coupled neural network (dpeaa)DE-He213 Harris hawks optimization (dpeaa)DE-He213 Mutual information entropy (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Peng, Xiaoxu verfasserin aut Kang, Lifei verfasserin aut Li, Yao verfasserin aut Jiang, Zichao verfasserin aut Sun, Kangjian verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:37-38 day:02 month:08 pages:28369-28392 https://dx.doi.org/10.1007/s11042-020-09228-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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 54.87 ASE AR 79 2020 37-38 02 08 28369-28392 |
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10.1007/s11042-020-09228-3 doi (DE-627)SPR041192788 (SPR)s11042-020-09228-3-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Jia, Heming verfasserin aut Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. Image segmentation (dpeaa)DE-He213 Pulse coupled neural network (dpeaa)DE-He213 Harris hawks optimization (dpeaa)DE-He213 Mutual information entropy (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Peng, Xiaoxu verfasserin aut Kang, Lifei verfasserin aut Li, Yao verfasserin aut Jiang, Zichao verfasserin aut Sun, Kangjian verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:37-38 day:02 month:08 pages:28369-28392 https://dx.doi.org/10.1007/s11042-020-09228-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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 54.87 ASE AR 79 2020 37-38 02 08 28369-28392 |
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10.1007/s11042-020-09228-3 doi (DE-627)SPR041192788 (SPR)s11042-020-09228-3-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Jia, Heming verfasserin aut Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. Image segmentation (dpeaa)DE-He213 Pulse coupled neural network (dpeaa)DE-He213 Harris hawks optimization (dpeaa)DE-He213 Mutual information entropy (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Peng, Xiaoxu verfasserin aut Kang, Lifei verfasserin aut Li, Yao verfasserin aut Jiang, Zichao verfasserin aut Sun, Kangjian verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:37-38 day:02 month:08 pages:28369-28392 https://dx.doi.org/10.1007/s11042-020-09228-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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 54.87 ASE AR 79 2020 37-38 02 08 28369-28392 |
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10.1007/s11042-020-09228-3 doi (DE-627)SPR041192788 (SPR)s11042-020-09228-3-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Jia, Heming verfasserin aut Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. Image segmentation (dpeaa)DE-He213 Pulse coupled neural network (dpeaa)DE-He213 Harris hawks optimization (dpeaa)DE-He213 Mutual information entropy (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Peng, Xiaoxu verfasserin aut Kang, Lifei verfasserin aut Li, Yao verfasserin aut Jiang, Zichao verfasserin aut Sun, Kangjian verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:37-38 day:02 month:08 pages:28369-28392 https://dx.doi.org/10.1007/s11042-020-09228-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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 54.87 ASE AR 79 2020 37-38 02 08 28369-28392 |
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Enthalten in Multimedia tools and applications 79(2020), 37-38 vom: 02. Aug., Seite 28369-28392 volume:79 year:2020 number:37-38 day:02 month:08 pages:28369-28392 |
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Image segmentation Pulse coupled neural network Harris hawks optimization Mutual information entropy Image entropy |
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Jia, Heming @@aut@@ Peng, Xiaoxu @@aut@@ Kang, Lifei @@aut@@ Li, Yao @@aut@@ Jiang, Zichao @@aut@@ Sun, Kangjian @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR041192788</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024639.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201102s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-020-09228-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR041192788</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-020-09228-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jia, Heming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). 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Jia, Heming |
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Jia, Heming ddc 070 bkl 54.87 misc Image segmentation misc Pulse coupled neural network misc Harris hawks optimization misc Mutual information entropy misc Image entropy Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation |
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070 004 ASE 54.87 bkl Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation Image segmentation (dpeaa)DE-He213 Pulse coupled neural network (dpeaa)DE-He213 Harris hawks optimization (dpeaa)DE-He213 Mutual information entropy (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 |
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ddc 070 bkl 54.87 misc Image segmentation misc Pulse coupled neural network misc Harris hawks optimization misc Mutual information entropy misc Image entropy |
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ddc 070 bkl 54.87 misc Image segmentation misc Pulse coupled neural network misc Harris hawks optimization misc Mutual information entropy misc Image entropy |
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Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation |
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Jia, Heming Peng, Xiaoxu Kang, Lifei Li, Yao Jiang, Zichao Sun, Kangjian |
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pulse coupled neural network based on harris hawks optimization algorithm for image segmentation |
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Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation |
| abstract |
Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. |
| abstractGer |
Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. |
| abstract_unstemmed |
Abstract Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. |
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| container_issue |
37-38 |
| title_short |
Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation |
| url |
https://dx.doi.org/10.1007/s11042-020-09228-3 |
| remote_bool |
true |
| author2 |
Peng, Xiaoxu Kang, Lifei Li, Yao Jiang, Zichao Sun, Kangjian |
| author2Str |
Peng, Xiaoxu Kang, Lifei Li, Yao Jiang, Zichao Sun, Kangjian |
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27135030X |
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c |
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false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-020-09228-3 |
| up_date |
2024-07-03T20:44:26.213Z |
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1803592103326908416 |
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| score |
7.3997507 |