{"title":"Medical Image Fusion using PCNN Optimized by Whale Optimization Algorithm","authors":"Ritwik Raha, Arpan Sengupta, Supriya Dhabal","doi":"10.1109/ICCE50343.2020.9290504","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel algorithm for the fusion of medical images using a modified Pulse Coupled Neural Network(PCNN) optimized by the Whale Optimization Algorithm(WOA). In this proposed algorithm the two images are passed through the WOA which computes the necessary parameters for the PCNN feature extractor through a multi-criteria fitness function. The features thus extracted are then used to fuse the images. The resultant fused image is judged by evaluating the entropy, Mutual Information (MI), Structural Similarity (SSIM), etc. as well as a specialized performance metric called feature mutual information.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"441 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE50343.2020.9290504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes a novel algorithm for the fusion of medical images using a modified Pulse Coupled Neural Network(PCNN) optimized by the Whale Optimization Algorithm(WOA). In this proposed algorithm the two images are passed through the WOA which computes the necessary parameters for the PCNN feature extractor through a multi-criteria fitness function. The features thus extracted are then used to fuse the images. The resultant fused image is judged by evaluating the entropy, Mutual Information (MI), Structural Similarity (SSIM), etc. as well as a specialized performance metric called feature mutual information.