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

Gerig, Guido and Welti, Daniel and Guttmann, Charles and Colchester, Alan C. F. and Szekely, Gabor (1998) Exploring the discrimination power of the time domain for segmentation and characterization of lesions in serial MR data. In: Wells, William M. and Colchester, Alan C. F. and Delp, Scott, eds. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 First International Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 469-480. ISBN 978-3-540-65136-9. E-ISBN 978-3-540-49563-5. (doi:10.1007/BFb0056232) (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) (KAR id:17762)

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.
Official URL:
http://dx.doi.org/10.1007/BFb0056232

Abstract

This paper presents a new methodology for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The purpose of the analysis is a detection and characterization of objects based on their dynamic changes. The technique was inspired by procedures developed for the analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a new 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 function over the whole time series. This leads to the hypothesis that static regions remain unchanged over time, whereas local changes in tissue characteristics cause typical functions in the voxel's time series. A set of features are derived from the time series and their derivatives, 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. Individual processing of a series of 3-D data sets is therefore replaced by a fully 4-D processing. To explore the sensitivity of time information, active lesions are segmented solely based on time fluctuation, neglecting absolute intensity information. The project is 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 high degree of automation. Further, an enhanced set of morphometric parameters might give a better insight into the course of the disease and therefore leads to a better understanding of the disease mechanism and of drug effects. The new method has been applied to two time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of roughly one year. The results demonstrate that time evolution is a highly sensitive feature to detect fluctuating structures.

Item Type: Book section
DOI/Identification number: 10.1007/BFb0056232
Additional 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: Multiple Sclerosis, Bias Correction, White Matter Lesion, Multiple Sclerosis Lesion, Active Lesion
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: R.F. Xu
Date Deposited: 30 Jun 2009 11:38 UTC
Last Modified: 16 Nov 2021 09:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17762 (The current URI for this page, for reference purposes)

University of Kent Author Information

Colchester, Alan C. F..

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