{"title":"基于本地不相似卡的图标注册","authors":"Hanae Mahmoudi, Hiba Ramadan, J. Riffi, H. Tairi","doi":"10.1109/ISCV54655.2022.9806120","DOIUrl":null,"url":null,"abstract":"In this work, we focus on iconic registration as one of the fundamental tasks in medical imaging. We have proposed a new approach that consists of the registration of two mono-modal images based on the local dissimilarity card (LDC). Instead of maximizing the similarity between the two input images as in traditional registration techniques, the main contribution of this work is minimizing the dissimilarity between them. Experiments in RMI medical images have shown that the proposed approach is as reliable as an original method using mutual information-based registration as a reference.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Iconic registration based on the local dissimilarity card\",\"authors\":\"Hanae Mahmoudi, Hiba Ramadan, J. Riffi, H. Tairi\",\"doi\":\"10.1109/ISCV54655.2022.9806120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we focus on iconic registration as one of the fundamental tasks in medical imaging. We have proposed a new approach that consists of the registration of two mono-modal images based on the local dissimilarity card (LDC). Instead of maximizing the similarity between the two input images as in traditional registration techniques, the main contribution of this work is minimizing the dissimilarity between them. Experiments in RMI medical images have shown that the proposed approach is as reliable as an original method using mutual information-based registration as a reference.\",\"PeriodicalId\":426665,\"journal\":{\"name\":\"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"volume\":\"2013 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCV54655.2022.9806120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCV54655.2022.9806120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iconic registration based on the local dissimilarity card
In this work, we focus on iconic registration as one of the fundamental tasks in medical imaging. We have proposed a new approach that consists of the registration of two mono-modal images based on the local dissimilarity card (LDC). Instead of maximizing the similarity between the two input images as in traditional registration techniques, the main contribution of this work is minimizing the dissimilarity between them. Experiments in RMI medical images have shown that the proposed approach is as reliable as an original method using mutual information-based registration as a reference.