Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data

Gerig, G. and Welti, C. and Guttmann, C.R.G and Colchester, A.C.F. and Szekely, G. (2000) Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Medical Image Analysis, 4 (1). pp. 31-42. ISSN 1361-8415 . (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1016/S1361-8415(00)00005-0

Abstract

This paper presents a new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The technique was inspired by procedures developed for analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness over the whole time series, Thus, static regions remain unchanged over time, whereas changes in tissue characteristics produce typical intensity variations in the voxel's time series. A set of features was derived from the times series, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster-Shafer's theory. The project was driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with a high degree of automation. The new approach replaces conventional segmentation of series of 3-D data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set. The new method has been applied to a total of 11 time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of about 1 year. The results demonstrate that time evolution is a highly sensitive feature for detection of fluctuating structures.

Item Type: Article
Additional information: Document Type: Proceedings Paper. Conference Information: 1st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 98) CAMBRIDGE, MASSACHUSETTS, OCT 11-13, 1998 Harvard Med Sch, Boston; Massachusetts Inst Technol, MA
Uncontrolled keywords: time series analysis; lesions in magnetic resonance imaging; temporal analysis; multiple sclerosis
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts
Faculties > Science Technology and Medical Studies > School of Biosciences > Biomedical Research Group
Depositing User: O.O. Odanye
Date Deposited: 21 May 2009 08:25
Last Modified: 24 Apr 2012 13:19
Resource URI: http://kar.kent.ac.uk/id/eprint/16134 (The current URI for this page, for reference purposes)
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