NDLI: 3D Segmentation with an Application of Level Set-Method using MRI Volumes for Image Guided Surgery

Content Provider	IEEE Xplore Digital Library
Author	Bosnjak, A. Montilla, G. Villegas, R. Jara, I.
Copyright Year	2007
Description	Author affiliation: Univ. de Carabobo, Valencia (Bosnjak, A.; Montilla, G.; Villegas, R.)
Abstract	This paper proposes an innovation in the application for image guided surgery using a comparative study of three different method of segmentation. This segmentation method is faster than the manual segmentation of images, with the advantage that it allows to use the same patient as anatomical reference, which has more precision than a generic atlas. This new methodology for 3D information extraction is based on a processing chain structured of the following modules: 1) 3D filtering: the purpose is to preserve the contours of the structures and to smooth the homogeneous areas; several filters were tested and finally an anisotropic diffusion filter was used. 2) 3D Segmentation. This module compares three different methods: Region growing Algorithm, Cubic spline hand assisted, and Level Set Method. It then proposes a Level Set-based on the front propagation method that allows the making of the reconstruction of the internal walls of the anatomical structures of the brain. 3) 3D visualization. The new contribution of this work consists on the visualization of the segmented model and its use in the pre-surgery planning.
Starting Page	5263
Ending Page	5266
File Size	540220
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4353529
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Magnetic resonance imaging Surgery Information filtering Information filters Visualization Technological innovation Data mining Testing Anisotropic magnetoresistance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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