The general application of the spherical mean value method for image noise reduction

Author

Li, Lin

Author_Institution

Dept. of Radiol., Pennsylvania Univ., Philadelphia, PA, USA

fYear

2002

fDate

2002

Firstpage

185

Lastpage

186

Abstract

A spherical mean value (SMV) method was shown to reduce image noise significantly for magnetic field and temperature mapping. Here the SMV method is proposed as a general image-processing tool, applicable to a wide variety of scalar or vector physical quantities including static electromagnetic, current density, flow velocity, gravity, and temperature fields

Keywords

Laplace equations; filtering theory; harmonic analysis; image processing; interference suppression; Laplace equation; averaging; current density; data noise; flow velocity; general image-processing tool; gravity fields; harmonic functions; image noise reduction; magnetic field gradient tensor; magnetic field mapping; multiple-order spatial derivatives; scalar physical quantities; spatial resolution; spherical mean value method; static electromagnetic fields; temperature mapping; vector physical quantities; Current density; Electromagnetic fields; Gravity; Laplace equations; Magnetic fields; Magnetic noise; Magnetic resonance; Magnetic susceptibility; Noise reduction; Temperature;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioengineering Conference, 2002. Proceedings of the IEEE 28th Annual Northeast

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-7419-3

Type

conf

DOI

10.1109/NEBC.2002.999527

Filename

999527