Segmentation of white matter lesions from volumetric MR images.

Hojjatoleslami, Ali and Kruggel, F. and von Cramon, D. Yves (1999) Segmentation of white matter lesions from volumetric MR images. Medical Image Computing and Computer-Assisted Intervention (MICCAI - 1999), 1679/1999 . pp. 52-61. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1007/10704282_6

Abstract

Quantitative analysis of the changes to the brain’s white matter is an important objective for a better understanding of pathological changes in various forms of degenerative brain diseases. To achieve an accurate quantification, an algorithm is proposed for automatic segmentation of white matter atrophies and lesions from T1-weighted 3D Magnetic Resonance (MR) images of the head. Firstly, white matter, gray matter and cerebrospinal fluid (CSF) compartments are segmented. Then, external and internal cisterns are separated by placing cutting planes relative to the position of the anterior and posterior commissure. Finally, a region growing method is applied to detect lesions inside the white matter. Since lesions may be adjacent to the gray matter, we use the external cisterns as a clue to prevent the algorithm from absorbing low gray level points in the gray matter. The method is fully applied to detect the white matter lesions and relevant structures from a set of 41 MR images of normal and pathological subjects. Subjective assessment of the results demonstrates a high performance and reliability of this method.

Item Type: Article
Subjects: R Medicine > R Medicine (General)
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Q Science > QA Mathematics (inc Computing science) > QA171 Representation theory
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Faculties > Science Technology and Medical Studies > School of Computing > Computational Intelligence Group
Faculties > Science Technology and Medical Studies > School of Biosciences > Biomedical Research Group
Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group > Natural Computation Group
Depositing User: Sayed Ali Hojjatoleslami
Date Deposited: 19 May 2011 11:47
Last Modified: 18 Jul 2014 11:33
Resource URI: http://kar.kent.ac.uk/id/eprint/27606 (The current URI for this page, for reference purposes)
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