Rakesh Kumar, Kristin J. Dana, P. Anandan, Neil E. Okamoto, J. Bergen, P. Hemler, T. Sumanaweera, P. Elsen, J. Adler
{"title":"Frameless registration of MR and CT 3D volumetric data sets","authors":"Rakesh Kumar, Kristin J. Dana, P. Anandan, Neil E. Okamoto, J. Bergen, P. Hemler, T. Sumanaweera, P. Elsen, J. Adler","doi":"10.1109/ACV.1994.341316","DOIUrl":null,"url":null,"abstract":"In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data sets with spatial filters so they can be directly matched. The matching is done by a direct optimization technique using a gradient based descent approach and a coarse-to-fine control strategy over a 4D pyramid. We present results on registering the head and spine data by matching 3D edges and results on registering cranial ventricle data by matching images filtered by a Laplacian of a Gaussian.<<ETX>>","PeriodicalId":437089,"journal":{"name":"Proceedings of 1994 IEEE Workshop on Applications of Computer Vision","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE Workshop on Applications of Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACV.1994.341316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data sets with spatial filters so they can be directly matched. The matching is done by a direct optimization technique using a gradient based descent approach and a coarse-to-fine control strategy over a 4D pyramid. We present results on registering the head and spine data by matching 3D edges and results on registering cranial ventricle data by matching images filtered by a Laplacian of a Gaussian.<>
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