Geometry-driven-diffusion filtering of magnetic resonance images using model-based conductance |
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Authors: | Ivan Bajla Igor Holländer |
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Affiliation: | (1) Austrian Research Centers Seibersdorf, Department of High-Performance Image Processing, 2444 Seibersdorf, Austria; Tel: (+)43 50550 2903; Fax: (+)43 2254 72133; e-mail: ivan.bajla@arcs.ac.at , SK;(2) University of Manchester, Manchester, UK , GB |
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Abstract: | The paper deals with the problems of staircase artifacts and low-contrast boundary smoothing in filtering (magnetic resonance
MR) brain tomograms that is based on geometry-driven diffusion (GDD). A novel method of the model-based GDD filtering of MR
brain tomograms is proposed to tackle these problems. It is based on a local adaptation of the conductance that is defined
for each diffusion iteration within the variable limits. The local adaptation uses a neighborhood inhomogeneity measure, pixel
dissimilarity, while gradient histograms of MR brain template regions are used as the variable limits for the conductance.
A methodology is developed for implementing the template image selected from an MR brain atlas to the model-based GDD filtering.
The proposed method is tested on an MR brain phantom. The methodology developed is exemplified on the real MR brain tomogram
with the corresponding template selected from the Brainweb. The performance of the developed algorithms is evaluated quantitatively
and visually.
Received: 1 September 1998 / Accepted: 20 August 2000 |
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Keywords: | : Nonlinear image filtering – Geometry-driven diffusion – Image segmentation – Model-based image processing – Magnetic resonance imaging |
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