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Using curve-fitting of curvilinear features for assessing registration of clinical neuropathology with in vivo MRI

Laissue, Philippe and Kenwright, Chris and Hojjatoleslami, Ali and Colchester, Alan C. F. (2008) Using curve-fitting of curvilinear features for assessing registration of clinical neuropathology with in vivo MRI. In: Metaxas, Dimitris and Axel, Leon and Fichtinger, Gabor and Szekely, Gabor, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008 11th International Conference. Lecture Notes in Computer Science, 2 . Springer, Berlin, Germany, 1050 -1057. ISBN 978-3-540-85989-5. E-ISBN 978-3-540-85990-1. (doi:10.1007/978-3-540-85990-1_126) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/978-3-540-85990-1_126

Abstract

Traditional neuropathological examination provides information about neurological disease or injury of a patient at a high-resolution level. Correlating this type of post mortem diagnosis with in vivo image data of the same patient acquired by non-invasive tomographic scans greatly complements the interpretation of any disease or injury. We present the validation of a registration method for correlating macroscopic pathological images with MR images of the same patient. This also allows for 3-D mapping of the distribution of pathological changes throughout the brain. As the validation deals with datasets of widely differing sampling, we propose a method using smooth Curvilinear anatomical features in the brain which allows interpolation between wide-spaced samples. Curvilinear features are common anatomically, and if selected carefully have the potential to allow determination of the accuracy of co-registration across large areas of a volume of interest.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-540-85990-1_126
Uncontrolled keywords: Lateral Ventricle, Optical Volume, Registration Procedure, Image Comp, Target Registration Error
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Sciences > School of Biosciences
Depositing User: M.P. Stone
Date Deposited: 15 Apr 2009 14:35 UTC
Last Modified: 29 May 2019 12:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/12171 (The current URI for this page, for reference purposes)
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