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Medical images from a single modality contain limited data about the organ. On the other hand, images from different modalities contain valuable structural and functio nal data about an organ. Medical image fusion (MIF) strategies integrate complementary information from two medical images captured using distinct modalities. This paper offered a new multimodal MIF approach using the parameter-adaptive pulse-coupled neural networks (PA-PCNN) within the non-subsampled contourlet transform (NSCT). The NSCT decomposes those images into high- and low-frequency bands. PA-PCNN combines those bands. The fused image was created using the inverse of the NSCT approach. To prove the proposed approach\u2019s performance, we appoint a variety of medical images like computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission CT (SPECT), and positron emission tomography (PET). Our experiments use five fusion metrics to validate the proposed approach\u2019s performance, such as entropy (EN), mutual information (MI), weighted edge information (Q<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{AB\/F}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mrow\/>\n                    <mml:mrow>\n                      <mml:mi>A<\/mml:mi>\n                      <mml:mi>B<\/mml:mi>\n                      <mml:mo>\/<\/mml:mo>\n                      <mml:mi>F<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>), nonlinear correlation information entropy (Q<jats:inline-formula><jats:alternatives><jats:tex-math>$$_{ncie}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mrow>\n                      <mml:mi>ncie<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>), and average gradient (AG). Outcomes show that the proposed approach achieves high overall performance in visual and objective characteristics when compared with five well-known MIF methods. The average values for EN, MI, Q<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{AB\/F}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mrow\/>\n                    <mml:mrow>\n                      <mml:mi>A<\/mml:mi>\n                      <mml:mi>B<\/mml:mi>\n                      <mml:mo>\/<\/mml:mo>\n                      <mml:mi>F<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, Q<jats:inline-formula><jats:alternatives><jats:tex-math>$$_{ncie}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mrow>\n                      <mml:mi>ncie<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and AG with the proposed approach are 5.2144,3.1282,.6600,.8071, and 8.9874, respectively.<\/jats:p>","DOI":"10.1007\/s11042-023-16515-2","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T09:02:54Z","timestamp":1693558974000},"page":"27379-27409","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Brain image fusion using the parameter adaptive-pulse coupled neural network (PA-PCNN) and non-subsampled contourlet transform (NSCT)"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1328-3217","authenticated-orcid":false,"given":"Sa. 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