Publication
Title
Volume quantization of the mouse cerebellum by quasi-automatic 3D segmentation of MR images
Author
Abstract
The aim of this work is the development of a non-invasive technique for efficient and accurate volume quantization of the cerebellum of mice. This enables an in-vivo study on the development of the cerebellum in order to define possible alterations in cerebellum volume of transgenic mice. We concentrate on a semi-automatic segmentation procedure to extract the cerebellum from 3D magnetic resonance data. The proposed technique uses a 3D variant of Vincent and Soille's immersion based watershed algorithm which is applied to the gradient magnitude of the MR data. The algorithm results in a partitioning of the data in volume primitives. The known drawback of the watershed algorithm, over-segmentation, is strongly reduced by a priori application of an adaptive anisotropic diffusion filter on the gradient magnitude data. In addition, over-segmentation is a posteriori contingently reduced by properly merging volume primitives, based on the minimum description length principle. The outcome of the preceding image processing step is presented to the user for manual segmentation. The first slice which contains the object of interest is quickly segmented by the user through selection of basic image regions. In the sequel, the subsequent slices are automatically segmented. The segmentation results are contingently manually corrected. The technique is tested on phantom objects, where segmentation errors less than 2% were observed. Three-dimensional reconstructions of the segmented data are shown for the mouse cerebellum and the mouse brains in toto.
Language
English
Source (book)
Proceedings of SPIE Medical Imaging, Newport Beach, California
Publication
Newport Beach, Calif. : 1996
ISBN
0-8194-2085-9
Volume/pages
p. 553-560
ISI
A1996BF76L00053
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.10.2008
Last edited 11.06.2017
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