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: 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2008, September 06-10, 2008, New York. (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)

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: Conference or workshop item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Science Technology and Medical Studies > School of Biosciences
Depositing User: M.P. Stone
Date Deposited: 15 Apr 2009 14:35
Last Modified: 23 Jun 2014 08:53
Resource URI: https://kar.kent.ac.uk/id/eprint/12171 (The current URI for this page, for reference purposes)
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