{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:09:08Z","timestamp":1774534148114,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T00:00:00Z","timestamp":1608163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801190"],"award-info":[{"award-number":["61801190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nature Science Foundation of Jilin Province","award":["20180101055JC"],"award-info":[{"award-number":["20180101055JC"]}]},{"DOI":"10.13039\/501100019077","name":"Outstanding Young Talent Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20180520029JH"],"award-info":[{"award-number":["20180520029JH"]}],"id":[{"id":"10.13039\/501100019077","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M611323"],"award-info":[{"award-number":["2017M611323"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Industrial \tTechnology Research and Development Funds of Jilin Province","award":["2019C054-3"],"award-info":[{"award-number":["2019C054-3"]}]},{"name":"National Key Research and Development Project of China","award":["2019YFC0409105"],"award-info":[{"award-number":["2019YFC0409105"]}]},{"name":"&quot;Thirteenth Five-Year Plan&quot; Scientific Research Planning Project of Education Department \tof Jilin Province","award":["JJKH20200678KJ,JJKH20200997KJ"],"award-info":[{"award-number":["JJKH20200678KJ,JJKH20200997KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In order to obtain the physiological information and key features of source images to the maximum extent, improve the visual effect and clarity of the fused image, and reduce the computation, a multi-modal medical image fusion framework based on feature reuse is proposed. The framework consists of intuitive fuzzy processing (IFP), capture image details network (CIDN), fusion, and decoding. First, the membership function of the image is redefined to remove redundant features and obtain the image with complete features. Then, inspired by DenseNet, we proposed a new encoder to capture all the medical information features in the source image. In the fusion layer, we calculate the weight of each feature graph in the required fusion coefficient according to the trajectory of the feature graph. Finally, the filtered medical information is spliced and decoded to reproduce the required fusion image. In the encoding and image reconstruction networks, the mixed loss function of cross entropy and structural similarity is adopted to greatly reduce the information loss in image fusion. To assess performance, we conducted three sets of experiments on medical images of different grayscales and colors. Experimental results show that the proposed algorithm has advantages not only in detail and structure recognition but also in visual features and time complexity compared with other algorithms.<\/jats:p>","DOI":"10.3390\/e22121423","type":"journal-article","created":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T21:21:49Z","timestamp":1608240109000},"page":"1423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multi-Modal Medical Image Fusion Based on FusionNet in YIQ Color Space"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-734X","authenticated-orcid":false,"given":"Kai","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"},{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4724-4726","authenticated-orcid":false,"given":"Xiongfei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"},{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0451-3503","authenticated-orcid":false,"given":"Hongrui","family":"Zang","sequence":"additional","affiliation":[{"name":"Information and Communication Company, State Grid Jilin Electric Power Co., Ltd., Changchun 130022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1496-9464","authenticated-orcid":false,"given":"Tiehu","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.inffus.2013.12.002","article-title":"Medical image fusion: A survey of the state of the art","volume":"9","author":"James","year":"2014","journal-title":"Inf. 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