鲸鱼优化算法优化的PCNN医学图像融合

Ritwik Raha, Arpan Sengupta, Supriya Dhabal
{"title":"鲸鱼优化算法优化的PCNN医学图像融合","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":"{\"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}","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

摘要

提出了一种基于鲸鱼优化算法(WOA)的改进脉冲耦合神经网络(PCNN)的医学图像融合算法。在该算法中,两幅图像通过WOA进行传递,WOA通过多准则适应度函数计算PCNN特征提取器所需的参数。这样提取的特征然后用于融合图像。通过评估熵、互信息(MI)、结构相似度(SSIM)等以及一种称为特征互信息的专门性能指标来判断融合后的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Medical Image Fusion using PCNN Optimized by Whale Optimization Algorithm
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Realization and Implementation of Optical Reversible Universal Quadruple Logic Gate (ORUQLG) Advanced Design of Transmitting Antenna System for Polarization Modulation Design and Development of Solar Power Hybrid Electric Vehicles Charging Station Performance Analysis of Multilingual Encryption for Enhancing Data Security using Cellular Automata based State Transition Mapping: A Linear Approach Online Handwritten Bangla and Devanagari Character Recognition by using CNN: A Deep Learning Concept
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1