Multimodal medical image fusion using PCNN optimized by the QPSO algorithm
[Display omitted] This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by...
Uložené v:
| Vydané v: | Applied soft computing Ročník 46; s. 588 - 595 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.09.2016
|
| Predmet: | |
| ISSN: | 1568-4946, 1872-9681 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | [Display omitted]
This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by the QPSO-PCNN model, respectively. Through the QPSO algorithm, the PCNN model could find the optimal parameters for the source images, A and B. To improve the efficiency and quality of QPSO, three evaluation criteria, image entropy (EN), average gradient (AG) and spatial frequency (SF) were selected as the hybrid fitness function. Then, the output of the fusion model was obtained by the judgment factor according to the firing maps of two source images, which maybe was the pixel value of the image A, or that of the image B, or the tradeoff value of them. Based on the output of the fusion model, the fused image was gained. Finally, we used five pairs of multimodal medical images as experimental data to test and verify the proposed method. Furthermore, the mutual information (MI), structural similarity (SSIM), image entropy (EN), etc. were used to judge the performances of different methods. The experimental results illustrated that the proposed method exhibited better performances. |
|---|---|
| AbstractList | [Display omitted]
This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by the QPSO-PCNN model, respectively. Through the QPSO algorithm, the PCNN model could find the optimal parameters for the source images, A and B. To improve the efficiency and quality of QPSO, three evaluation criteria, image entropy (EN), average gradient (AG) and spatial frequency (SF) were selected as the hybrid fitness function. Then, the output of the fusion model was obtained by the judgment factor according to the firing maps of two source images, which maybe was the pixel value of the image A, or that of the image B, or the tradeoff value of them. Based on the output of the fusion model, the fused image was gained. Finally, we used five pairs of multimodal medical images as experimental data to test and verify the proposed method. Furthermore, the mutual information (MI), structural similarity (SSIM), image entropy (EN), etc. were used to judge the performances of different methods. The experimental results illustrated that the proposed method exhibited better performances. |
| Author | Xu, Xinzheng Shan, Dong Wang, Guanying Jiang, Xiangying |
| Author_xml | – sequence: 1 givenname: Xinzheng orcidid: 0000-0001-6973-799X surname: Xu fullname: Xu, Xinzheng email: xxzheng@cumt.edu.cn, xuxinzh@163.com organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 2 givenname: Dong surname: Shan fullname: Shan, Dong organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 3 givenname: Guanying surname: Wang fullname: Wang, Guanying organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 4 givenname: Xiangying surname: Jiang fullname: Jiang, Xiangying organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China |
| BookMark | eNp9kM9OwzAMhyM0JMbgBTjlBVrctGtTiQua-KuxDQHnKE3cLVPXTEmGNJ6eTOPEYRfbh99n2d8lGfS2R0JuMkgzyMrbdSq9VSmLcwp5CoyfkWHGK5bUJc8GcR6XPCnqorwgl96vIQZrxofk9W3XBbOxWnZ0g9qo2M1GLpG2O29sT2Ptl3Qxmc2o3cak-UFNmz0NK6Tvi485ld3SOhNWmyty3srO4_VfH5Gvx4fPyXMynT-9TO6nicoBQsKakrOqlagV8lxLqAFBj6GSqlSc51C0bc6qpmoKWYBigJJhWXCmai110-Qjwo57lbPeO2zF1sWT3V5kIA42xFocbIiDDQG5iDYixP9BygQZ4ofBSdOdRu-OKManvg064ZXBXkVbDlUQ2ppT-C-G134F |
| CitedBy_id | crossref_primary_10_1016_j_sigpro_2021_108108 crossref_primary_10_1016_j_sigpro_2018_08_002 crossref_primary_10_1049_iet_ipr_2017_0573 crossref_primary_10_1007_s00138_022_01322_w crossref_primary_10_1007_s11220_022_00391_5 crossref_primary_10_1007_s11517_020_02209_6 crossref_primary_10_1109_JSEN_2018_2822712 crossref_primary_10_1109_TNNLS_2023_3293274 crossref_primary_10_1016_j_asoc_2016_11_033 crossref_primary_10_26634_jele_15_3_21732 crossref_primary_10_3390_app8101977 crossref_primary_10_1016_j_infrared_2017_01_012 crossref_primary_10_1016_j_eswa_2021_114576 crossref_primary_10_1109_ACCESS_2020_2990607 crossref_primary_10_1007_s12559_021_09958_y crossref_primary_10_3390_electronics12122659 crossref_primary_10_1016_j_inffus_2021_06_007 crossref_primary_10_1109_ACCESS_2020_3047960 crossref_primary_10_1109_TCSVT_2020_2998696 crossref_primary_10_1109_ACCESS_2019_2924033 crossref_primary_10_3390_app11125524 crossref_primary_10_1109_TMECH_2024_3442782 crossref_primary_10_1007_s00500_017_2694_4 crossref_primary_10_1007_s10278_021_00554_y crossref_primary_10_1109_TIM_2024_3522423 crossref_primary_10_1016_j_neucom_2018_05_028 crossref_primary_10_1016_j_bspc_2020_101885 crossref_primary_10_3390_rs16050889 crossref_primary_10_1016_j_cor_2022_105937 crossref_primary_10_1016_j_sigpro_2020_107921 crossref_primary_10_32604_cmc_2021_016131 crossref_primary_10_3390_fi9040061 crossref_primary_10_1109_ACCESS_2019_2908076 crossref_primary_10_1016_j_ins_2020_05_100 crossref_primary_10_1109_LSP_2020_2989054 crossref_primary_10_1155_2017_8407019 crossref_primary_10_1007_s10589_022_00362_2 crossref_primary_10_1016_j_apm_2021_03_059 crossref_primary_10_1016_j_bbe_2017_12_005 crossref_primary_10_3389_feart_2024_1493749 crossref_primary_10_1016_j_epsr_2021_107398 crossref_primary_10_1080_10106049_2022_2134464 crossref_primary_10_1016_j_infrared_2018_04_004 crossref_primary_10_1109_TIM_2018_2865046 crossref_primary_10_1109_TRPMS_2018_2890359 crossref_primary_10_1002_ima_22393 crossref_primary_10_1109_ACCESS_2018_2822688 crossref_primary_10_3390_en12122270 crossref_primary_10_1007_s10489_021_02282_w crossref_primary_10_1007_s13369_020_05201_2 crossref_primary_10_1142_S0219467825500391 crossref_primary_10_1016_j_bspc_2019_101724 crossref_primary_10_1371_journal_pone_0286024 crossref_primary_10_1016_j_bspc_2022_103762 crossref_primary_10_1016_j_ijleo_2019_05_007 crossref_primary_10_1002_ima_22436 crossref_primary_10_1016_j_bspc_2017_01_003 crossref_primary_10_1088_1361_6501_aaa33a crossref_primary_10_4018_IJCVIP_2018070102 crossref_primary_10_1007_s11042_020_08834_5 crossref_primary_10_1016_j_neucom_2018_09_018 crossref_primary_10_1007_s11042_018_6099_x crossref_primary_10_1016_j_asoc_2022_109631 crossref_primary_10_1007_s11831_018_9253_8 crossref_primary_10_1063_5_0056983 crossref_primary_10_1080_10106049_2021_2009920 crossref_primary_10_3389_fnbot_2022_1050981 crossref_primary_10_1016_j_infrared_2018_06_002 crossref_primary_10_1080_08982112_2017_1322210 crossref_primary_10_1109_ACCESS_2018_2845855 crossref_primary_10_1515_revneuro_2025_0062 crossref_primary_10_1155_2022_3398810 crossref_primary_10_1016_j_asr_2018_08_006 crossref_primary_10_1016_j_eswa_2020_113592 crossref_primary_10_1016_j_ecoinf_2021_101426 crossref_primary_10_3390_app7040415 crossref_primary_10_1109_TIM_2022_3216403 crossref_primary_10_1080_09500340_2020_1718789 crossref_primary_10_1007_s11517_022_02697_8 crossref_primary_10_1016_j_bspc_2021_102536 crossref_primary_10_3233_JIFS_210430 crossref_primary_10_1016_j_bspc_2018_05_042 crossref_primary_10_1038_s41598_025_13862_y crossref_primary_10_1016_j_imavis_2025_105581 crossref_primary_10_1049_iet_ipr_2017_0214 crossref_primary_10_1016_j_infrared_2018_04_017 |
| Cites_doi | 10.1109/CCDC.2008.4597791 10.4156/jdcta.vol6.issue20.54 10.1007/s11517-012-0943-3 10.4304/jcp.6.8.1546-1553 10.1109/TCOM.1983.1095851 10.1162/neco.1990.2.3.293 10.1002/tee.20684 10.1109/TIP.2003.819861 10.1016/S1361-8415(03)00015-X 10.3233/BME-130802 10.4103/0256-4602.64601 10.1016/j.inffus.2007.04.003 10.1016/0167-8655(89)90004-4 10.1016/j.patcog.2010.01.011 10.1016/j.compmedimag.2013.08.003 10.1179/1743131X12Y.0000000016 |
| ContentType | Journal Article |
| Copyright | 2016 Elsevier B.V. |
| Copyright_xml | – notice: 2016 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.asoc.2016.03.028 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-9681 |
| EndPage | 595 |
| ExternalDocumentID | 10_1016_j_asoc_2016_03_028 S1568494616301570 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-2b6827faedce83da090e0d507ac6c88304ff327b7b4a40c20ea2e6482c9dadbb3 |
| ISICitedReferencesCount | 103 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000377999900044&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 |
| IngestDate | Sat Nov 29 03:05:29 EST 2025 Tue Nov 18 21:45:30 EST 2025 Fri Feb 23 02:24:49 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Pulse-coupled neural networks Multimodal medical image fusion Mutual information Quantum-behaved particle swarm optimization algorithm |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-2b6827faedce83da090e0d507ac6c88304ff327b7b4a40c20ea2e6482c9dadbb3 |
| ORCID | 0000-0001-6973-799X |
| PageCount | 8 |
| ParticipantIDs | crossref_primary_10_1016_j_asoc_2016_03_028 crossref_citationtrail_10_1016_j_asoc_2016_03_028 elsevier_sciencedirect_doi_10_1016_j_asoc_2016_03_028 |
| PublicationCentury | 2000 |
| PublicationDate | September 2016 2016-09-00 |
| PublicationDateYYYYMMDD | 2016-09-01 |
| PublicationDate_xml | – month: 09 year: 2016 text: September 2016 |
| PublicationDecade | 2010 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2016 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Zhao, Zhao, Hao (bib0135) 2014; 24 Ma, Li, Wang (bib0095) 2006 Wang, Bovik, Sheikh (bib0140) 2004; 13 Wang, Cong (bib0060) 2008 Sun, Feng, Xu (bib0110) 2004 Zhao, Ding (bib0080) 2014; 10 Toet (bib0025) 1989; 9 Hage, Hamade (bib0065) 2013; 37 Xu, Ding, Shi, Zhu, Zhao (bib0070) 2012; 25 Burt, Adelson (bib0150) 1983; COM-31 He, Meng, Wang (bib0020) 2011 Behrenbruch, Marias, Armitage (bib0005) 2003; 7 Zhao (bib0045) 2013 Eckhorn, Reitboeck, Arndt (bib0100) 1990; 2 Seetha, MuraliKrishna, Deekshatulu (bib0130) 2005 Wang, Li, Tian (bib0015) 2013; 61 Wang, Ma (bib0010) 2007 Li, Wu (bib0120) 2014; 42 Wang, Xu, Jiang, Nie (bib0155) 2015; 6 Kennedy, Eberhart (bib0105) 1995 Das, Kundu (bib0040) 2012; 50 Eckhorn, Reitboeck, Arndt (bib0030) 1989 Zhang, Mabu, Hirasawa (bib0050) 2011; 6 Xu, Ding, Zhao, Zhu (bib0055) 2011; 6 Ma, Dai, Li (bib0115) 2002; 23 Sun, Lai, Xu (bib0085) 2007 Wang, Ma (bib0035) 2008; 9 Wang, Ma, Gu (bib0145) 2010; 43 Fang, Sun, Ding (bib0090) 2010; 27 Jiang (bib0075) 2012; 6 Ma, Zhan, Wang (bib0125) 2010 Zhao (10.1016/j.asoc.2016.03.028_bib0135) 2014; 24 Wang (10.1016/j.asoc.2016.03.028_bib0140) 2004; 13 Ma (10.1016/j.asoc.2016.03.028_bib0095) 2006 Wang (10.1016/j.asoc.2016.03.028_bib0145) 2010; 43 Kennedy (10.1016/j.asoc.2016.03.028_bib0105) 1995 Wang (10.1016/j.asoc.2016.03.028_bib0035) 2008; 9 Wang (10.1016/j.asoc.2016.03.028_bib0010) 2007 Xu (10.1016/j.asoc.2016.03.028_bib0055) 2011; 6 Xu (10.1016/j.asoc.2016.03.028_bib0070) 2012; 25 Zhao (10.1016/j.asoc.2016.03.028_bib0080) 2014; 10 Wang (10.1016/j.asoc.2016.03.028_bib0155) 2015; 6 Hage (10.1016/j.asoc.2016.03.028_bib0065) 2013; 37 He (10.1016/j.asoc.2016.03.028_bib0020) 2011 Behrenbruch (10.1016/j.asoc.2016.03.028_bib0005) 2003; 7 Fang (10.1016/j.asoc.2016.03.028_bib0090) 2010; 27 Wang (10.1016/j.asoc.2016.03.028_bib0015) 2013; 61 Eckhorn (10.1016/j.asoc.2016.03.028_bib0030) 1989 Wang (10.1016/j.asoc.2016.03.028_bib0060) 2008 Ma (10.1016/j.asoc.2016.03.028_bib0125) 2010 Seetha (10.1016/j.asoc.2016.03.028_bib0130) 2005 Sun (10.1016/j.asoc.2016.03.028_bib0085) 2007 Li (10.1016/j.asoc.2016.03.028_bib0120) 2014; 42 Das (10.1016/j.asoc.2016.03.028_bib0040) 2012; 50 Sun (10.1016/j.asoc.2016.03.028_bib0110) 2004 Burt (10.1016/j.asoc.2016.03.028_bib0150) 1983; COM-31 Jiang (10.1016/j.asoc.2016.03.028_bib0075) 2012; 6 Zhang (10.1016/j.asoc.2016.03.028_bib0050) 2011; 6 Ma (10.1016/j.asoc.2016.03.028_bib0115) 2002; 23 Toet (10.1016/j.asoc.2016.03.028_bib0025) 1989; 9 Eckhorn (10.1016/j.asoc.2016.03.028_bib0100) 1990; 2 Zhao (10.1016/j.asoc.2016.03.028_bib0045) 2013 |
| References_xml | – volume: 6 start-page: 1546 year: 2011 end-page: 1553 ident: bib0055 article-title: Particle swarm optimization for automatic parameters determination of pulse coupled neural network publication-title: J. Comput. – volume: 6 start-page: 501 year: 2012 end-page: 509 ident: bib0075 article-title: A self-adapting pulse-coupled neural network based on modified differential evolution algorithm and its application on image segmentation publication-title: Int. J. Digit. Content Technol. Appl. – volume: 2 start-page: 293 year: 1990 end-page: 307 ident: bib0100 article-title: Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex publication-title: Neural Comput. – volume: 25 start-page: 909 year: 2012 end-page: 915 ident: bib0070 article-title: A self-adaptive method for optimization the parameters of pulse coupled neural network based QPSO algorithm publication-title: Pattern Recognit. Artif. Intell. – start-page: 326 year: 2004 end-page: 331 ident: bib0110 article-title: Particle swarm optimization with particles having quantum behavior publication-title: Congress on Evolutionary Computation, Portland, USA – volume: 37 start-page: 466 year: 2013 end-page: 474 ident: bib0065 article-title: Segmentation of histology slides of cortical bone using pulse coupled neural networks optimized by particle-swarm optimization publication-title: Comput. Med. Imaging Graphics – volume: 6 start-page: 474 year: 2011 end-page: 482 ident: bib0050 article-title: Image denoising using pulse coupled neural network with an adaptive Pareto genetic algorithm publication-title: IEEJ Trans. Electr. Electron. Eng. – year: 1989 ident: bib0030 article-title: A neural network for feature linking via synchronous activity: Results from cat visual cortex and from simulations publication-title: Models of Brain Function – volume: 27 start-page: 336 year: 2010 end-page: 347 ident: bib0090 article-title: A review of quantum-behaved particle swarm optimization publication-title: IETE Tech. Rev. – volume: 43 start-page: 2003 year: 2010 end-page: 2016 ident: bib0145 article-title: Multi-focus image fusion using PCNN publication-title: Pattern Recognit. – volume: COM-31 start-page: 532 year: 1983 end-page: 540 ident: bib0150 article-title: The Laplacian pyramid as a compact image code publication-title: IEEE Trans. Commun. – volume: 23 start-page: 46 year: 2002 end-page: 51 ident: bib0115 article-title: Automated image segmentation using pulse coupled neural networks and images entropy publication-title: J. China Inst. Commun. – start-page: 1 year: 2006 end-page: 20 ident: bib0095 article-title: Principle of Pulse-coupled Neural Network and its Applications – start-page: 83 year: 2010 end-page: 109 ident: bib0125 article-title: Image Fusion Applications of Pulse-Coupled Neural Network – start-page: 755 year: 2007 end-page: 759 ident: bib0010 article-title: Dual-channel PCNN and its application in the field of image fusion publication-title: International Conference on Natural Computation, Haikou, China – volume: 7 start-page: 311 year: 2003 end-page: 340 ident: bib0005 article-title: Fusion of contrast-enhanced breast MR and mammographic imaging data publication-title: Med. Image Anal. – start-page: 2842 year: 2005 end-page: 2845 ident: bib0130 article-title: Data fusion performance analysis based on conventional and wavelet transform techniques publication-title: IEEE Proceedings on Geoscience and Remote Sensing Symposium, vol. 4 – volume: 9 start-page: 255 year: 1989 end-page: 261 ident: bib0025 article-title: A morphological pyramidal image decomposition publication-title: Pattern Recognit. Lett. – year: 2013 ident: bib0045 article-title: The PCNN adaptive segmentation algorithm based on visual perception publication-title: Third International Conference on Photonics and Image in Agriculture Engineering, Sanya, China – start-page: 597 year: 2011 end-page: 600 ident: bib0020 article-title: Contrast pyramid based image fusion scheme for infrared image and visible image publication-title: 2011 IEEE International conference on Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada – volume: 61 start-page: 529 year: 2013 end-page: 540 ident: bib0015 article-title: A novel multi-modal medical image fusion method based on shift-invariant shearlet transform publication-title: Imaging Sci. J. – volume: 10 start-page: 6635 year: 2014 end-page: 6642 ident: bib0080 article-title: Study of automated PCNN system based on fruit fly optimization algorithm publication-title: J. Comput. Inf. Syst. – volume: 6 start-page: 2523 year: 2015 end-page: 2530 ident: bib0155 article-title: A modified model of pulse coupled neural networks with adaptive parameters and its application on image fusion publication-title: ICIC Express Lett. – volume: 9 start-page: 176 year: 2008 end-page: 185 ident: bib0035 article-title: Medical image fusion using m-PCNN publication-title: Inf. Fusion – start-page: 294 year: 2007 end-page: 301 ident: bib0085 article-title: A modified quantum-behaved particle swarm optimization publication-title: 7th International Conference on Computational Science, Beijing, China – volume: 42 start-page: 217 year: 2014 end-page: 222 ident: bib0120 article-title: A novel image fusion method using self-adaptive dual-channel pulse coupled neural networks based on PSO evolutionary learning publication-title: Acta Electron. Sinica – volume: 24 start-page: 221 year: 2014 end-page: 228 ident: bib0135 article-title: Multimodal medical image fusion using improved multi-channel PCNN publication-title: Bio-medical Mater. Eng. – volume: 50 start-page: 1105 year: 2012 end-page: 1114 ident: bib0040 article-title: NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency publication-title: Med. Biol. Eng. Comput. – start-page: 1942 year: 1995 end-page: 1948 ident: bib0105 article-title: Particle swarm optimization publication-title: IEEE International Conference on Neural Networks, Perth, WA – start-page: 2576 year: 2008 end-page: 2579 ident: bib0060 article-title: Grayscale image edge detection based on pulse-coupled neural network and particle swarm optimization publication-title: 2008 Chinese Control and Decision Conference (CCDC) – volume: 13 start-page: 600 year: 2004 end-page: 612 ident: bib0140 article-title: Image quality assessment: from error visibility to structural similarity publication-title: IEEE Trans. Image Process. – start-page: 2576 year: 2008 ident: 10.1016/j.asoc.2016.03.028_bib0060 article-title: Grayscale image edge detection based on pulse-coupled neural network and particle swarm optimization publication-title: 2008 Chinese Control and Decision Conference (CCDC) doi: 10.1109/CCDC.2008.4597791 – start-page: 326 year: 2004 ident: 10.1016/j.asoc.2016.03.028_bib0110 article-title: Particle swarm optimization with particles having quantum behavior – volume: 6 start-page: 501 issue: 20 year: 2012 ident: 10.1016/j.asoc.2016.03.028_bib0075 article-title: A self-adapting pulse-coupled neural network based on modified differential evolution algorithm and its application on image segmentation publication-title: Int. J. Digit. Content Technol. Appl. doi: 10.4156/jdcta.vol6.issue20.54 – volume: 50 start-page: 1105 issue: 10 year: 2012 ident: 10.1016/j.asoc.2016.03.028_bib0040 article-title: NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-012-0943-3 – start-page: 2842 year: 2005 ident: 10.1016/j.asoc.2016.03.028_bib0130 article-title: Data fusion performance analysis based on conventional and wavelet transform techniques – volume: 6 start-page: 1546 issue: 8 year: 2011 ident: 10.1016/j.asoc.2016.03.028_bib0055 article-title: Particle swarm optimization for automatic parameters determination of pulse coupled neural network publication-title: J. Comput. doi: 10.4304/jcp.6.8.1546-1553 – start-page: 597 year: 2011 ident: 10.1016/j.asoc.2016.03.028_bib0020 article-title: Contrast pyramid based image fusion scheme for infrared image and visible image – year: 1989 ident: 10.1016/j.asoc.2016.03.028_bib0030 article-title: A neural network for feature linking via synchronous activity: Results from cat visual cortex and from simulations – start-page: 755 year: 2007 ident: 10.1016/j.asoc.2016.03.028_bib0010 article-title: Dual-channel PCNN and its application in the field of image fusion – volume: COM-31 start-page: 532 issue: 4 year: 1983 ident: 10.1016/j.asoc.2016.03.028_bib0150 article-title: The Laplacian pyramid as a compact image code publication-title: IEEE Trans. Commun. doi: 10.1109/TCOM.1983.1095851 – volume: 2 start-page: 293 issue: 3 year: 1990 ident: 10.1016/j.asoc.2016.03.028_bib0100 article-title: Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex publication-title: Neural Comput. doi: 10.1162/neco.1990.2.3.293 – volume: 6 start-page: 474 issue: 5 year: 2011 ident: 10.1016/j.asoc.2016.03.028_bib0050 article-title: Image denoising using pulse coupled neural network with an adaptive Pareto genetic algorithm publication-title: IEEJ Trans. Electr. Electron. Eng. doi: 10.1002/tee.20684 – volume: 13 start-page: 600 issue: 4 year: 2004 ident: 10.1016/j.asoc.2016.03.028_bib0140 article-title: Image quality assessment: from error visibility to structural similarity publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 25 start-page: 909 issue: 6 year: 2012 ident: 10.1016/j.asoc.2016.03.028_bib0070 article-title: A self-adaptive method for optimization the parameters of pulse coupled neural network based QPSO algorithm publication-title: Pattern Recognit. Artif. Intell. – start-page: 1 year: 2006 ident: 10.1016/j.asoc.2016.03.028_bib0095 – year: 2013 ident: 10.1016/j.asoc.2016.03.028_bib0045 article-title: The PCNN adaptive segmentation algorithm based on visual perception – volume: 42 start-page: 217 issue: 2 year: 2014 ident: 10.1016/j.asoc.2016.03.028_bib0120 article-title: A novel image fusion method using self-adaptive dual-channel pulse coupled neural networks based on PSO evolutionary learning publication-title: Acta Electron. Sinica – start-page: 83 year: 2010 ident: 10.1016/j.asoc.2016.03.028_bib0125 – volume: 23 start-page: 46 issue: 1 year: 2002 ident: 10.1016/j.asoc.2016.03.028_bib0115 article-title: Automated image segmentation using pulse coupled neural networks and images entropy publication-title: J. China Inst. Commun. – volume: 7 start-page: 311 issue: 3 year: 2003 ident: 10.1016/j.asoc.2016.03.028_bib0005 article-title: Fusion of contrast-enhanced breast MR and mammographic imaging data publication-title: Med. Image Anal. doi: 10.1016/S1361-8415(03)00015-X – volume: 24 start-page: 221 issue: 1 year: 2014 ident: 10.1016/j.asoc.2016.03.028_bib0135 article-title: Multimodal medical image fusion using improved multi-channel PCNN publication-title: Bio-medical Mater. Eng. doi: 10.3233/BME-130802 – volume: 27 start-page: 336 issue: 4 year: 2010 ident: 10.1016/j.asoc.2016.03.028_bib0090 article-title: A review of quantum-behaved particle swarm optimization publication-title: IETE Tech. Rev. doi: 10.4103/0256-4602.64601 – start-page: 1942 year: 1995 ident: 10.1016/j.asoc.2016.03.028_bib0105 article-title: Particle swarm optimization – volume: 9 start-page: 176 issue: 2 year: 2008 ident: 10.1016/j.asoc.2016.03.028_bib0035 article-title: Medical image fusion using m-PCNN publication-title: Inf. Fusion doi: 10.1016/j.inffus.2007.04.003 – volume: 10 start-page: 6635 issue: 15 year: 2014 ident: 10.1016/j.asoc.2016.03.028_bib0080 article-title: Study of automated PCNN system based on fruit fly optimization algorithm publication-title: J. Comput. Inf. Syst. – volume: 6 start-page: 2523 issue: 9 year: 2015 ident: 10.1016/j.asoc.2016.03.028_bib0155 article-title: A modified model of pulse coupled neural networks with adaptive parameters and its application on image fusion publication-title: ICIC Express Lett. – volume: 9 start-page: 255 issue: 4 year: 1989 ident: 10.1016/j.asoc.2016.03.028_bib0025 article-title: A morphological pyramidal image decomposition publication-title: Pattern Recognit. Lett. doi: 10.1016/0167-8655(89)90004-4 – volume: 43 start-page: 2003 issue: 6 year: 2010 ident: 10.1016/j.asoc.2016.03.028_bib0145 article-title: Multi-focus image fusion using PCNN publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2010.01.011 – start-page: 294 year: 2007 ident: 10.1016/j.asoc.2016.03.028_bib0085 article-title: A modified quantum-behaved particle swarm optimization – volume: 37 start-page: 466 issue: 7–8 year: 2013 ident: 10.1016/j.asoc.2016.03.028_bib0065 article-title: Segmentation of histology slides of cortical bone using pulse coupled neural networks optimized by particle-swarm optimization publication-title: Comput. Med. Imaging Graphics doi: 10.1016/j.compmedimag.2013.08.003 – volume: 61 start-page: 529 issue: 7 year: 2013 ident: 10.1016/j.asoc.2016.03.028_bib0015 article-title: A novel multi-modal medical image fusion method based on shift-invariant shearlet transform publication-title: Imaging Sci. J. doi: 10.1179/1743131X12Y.0000000016 |
| SSID | ssj0016928 |
| Score | 2.480344 |
| Snippet | [Display omitted]
This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 588 |
| SubjectTerms | Multimodal medical image fusion Mutual information Pulse-coupled neural networks Quantum-behaved particle swarm optimization algorithm |
| Title | Multimodal medical image fusion using PCNN optimized by the QPSO algorithm |
| URI | https://dx.doi.org/10.1016/j.asoc.2016.03.028 |
| Volume | 46 |
| WOSCitedRecordID | wos000377999900044&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-9681 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016928 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZgy4ELb0R5yQduqyBvHo59rEoRrNCyqAVyi-zE7qbqJtVugtr-esax410KVPTAJYqcZPL4Po3Hk3kg9MbUK5GpKgKmExrEkdCBgJkoSApgi2ZpzPuoym-f0tmMZRmfuz-m676dQFrX7Pycn_1XqGEMwDapszeA2wuFAdgH0GELsMP2n4DvU2qXTenSQgwG1dJE5ujOeMbGXe8dmO_PZuMG9MWyunRGKBDmy_zw81icHjerql0stw3XwVpdg9ru49C7dpj0AK6sM1BlVX25UJvRw4X1rr5rNmPfnX8aqFlfbEmYVu5AZnb8EeePmFAfcOVVKGVBzJ1j0enYeFtJJraRn5tvE9tk8zdVbr0KJ28FsNSE4NG-GK3LJP-lbvaV-cxHGQ4BbCe5kZEbGTmJcpBxG-2EacLZCO3sfTzIpv6_E-V9N17_Di7NykYEXn2SP5syW-bJ0QN0z60r8J7lw0N0S9WP0P2hZwd2Kvwxmm7ogR09cE8PbOmBe3pgQw_s6YHlBQZ6YEMP7OnxBH19f3C0_yFw_TSCIiKkDUJJWZhqYQJ_WVQKwokiJSwIREELxiISax2FqUxlLGJShESJUNGYhQUvRSll9BSN6qZWzxBWiaJMghRJSjBBBWdaT7SUpY4E1ZN4F02GT5MXrti86Xlymv8dlF009tec2VIr156dDF88d8aiNQJzINA11z2_0V1eoLsbjr9Eo3bVqVfoTvGjrdar1449PwFSwovy |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multimodal+medical+image+fusion+using+PCNN+optimized+by+the+QPSO+algorithm&rft.jtitle=Applied+soft+computing&rft.au=Xu%2C+Xinzheng&rft.au=Shan%2C+Dong&rft.au=Wang%2C+Guanying&rft.au=Jiang%2C+Xiangying&rft.date=2016-09-01&rft.issn=1568-4946&rft.volume=46&rft.spage=588&rft.epage=595&rft_id=info:doi/10.1016%2Fj.asoc.2016.03.028&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_asoc_2016_03_028 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |