{"title":"Elastic Medical Image Registration Based on Image Intensity","authors":"Xiuying Wang, D. Feng","doi":"10.1142/S0219467805001793","DOIUrl":null,"url":null,"abstract":"An automatic elastic medical image registration approach is proposed, based on image intensity. The algorithm is divided into two steps. In Step 1, global affine registration is first used to establish an initial guess and the resulting images can be assumed to have only small local elastic deformations. The mapped images are then used as inputs in Step 2, during which, the study image is modeled as elastic sheet by being divided into sub-images. Moving the individual sub-image in the reference image, the local displacement vectors are found and the global elastic transformation is achieved by assimilating all of the local transformation into a continuous transformation. The algorithm has been validated by simulated data, noisy data and clinical tomographic data. Both experiments and theoretical analysis have demonstrated that the proposed algorithm has a superior computational performance and can register images automatically with an improved accuracy.","PeriodicalId":142600,"journal":{"name":"Pan-Sydney Area Workshop on Visual Information Processing","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pan-Sydney Area Workshop on Visual Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S0219467805001793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
An automatic elastic medical image registration approach is proposed, based on image intensity. The algorithm is divided into two steps. In Step 1, global affine registration is first used to establish an initial guess and the resulting images can be assumed to have only small local elastic deformations. The mapped images are then used as inputs in Step 2, during which, the study image is modeled as elastic sheet by being divided into sub-images. Moving the individual sub-image in the reference image, the local displacement vectors are found and the global elastic transformation is achieved by assimilating all of the local transformation into a continuous transformation. The algorithm has been validated by simulated data, noisy data and clinical tomographic data. Both experiments and theoretical analysis have demonstrated that the proposed algorithm has a superior computational performance and can register images automatically with an improved accuracy.