Geometry-driven diffusion smoothing of the MR-brain images using a novel variable conductance

Author

Bajla, I.

Author_Institution

Inst. of Meas. Sci., Slovak Acad. of Sci., Bratislava

Volume

2

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

743

Abstract

A novel variable conductance function for geometry-driven diffusion smoothing is developed. It is bared on the measure of the neighborhood anisotropism adopted from the adaptive linear convolution image filtering. The results of several smoothing methods, aimed at the improvement of 3D visualization of MRI tomograms of the brain, are demonstrated on a phantom study and by two measures of signal-to-noise ratio

Keywords

biomedical NMR; brain; computerised tomography; convolution; image segmentation; medical image processing; smoothing methods; 3D visualization; MRI brain images; MRI tomograms; adaptive linear convolution image filtering; geometry-driven diffusion smoothing; neighborhood anisotropism; phantom study; signal-to-noise ratio; variable conductance; Adaptive filters; Anisotropic magnetoresistance; Convolution; Filtering; Imaging phantoms; Magnetic resonance imaging; Nonlinear filters; Signal to noise ratio; Smoothing methods; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.651955

Filename

651955