Name
Title | ||
|---|---|---|
Nonlinear Smoothing of MR Images Using Approximate Entropy - A Local Measure of Signal Intensity Irregularity |
Abstract | ||
|---|---|---|
Approximate entropy (ApEn) is a computable measure of sequential irregularity that is applicable to sequences of numbers of finite length. As such, it may be used to determine how random a sequence of numbers is. We exploit this property to determine the relevance of image information; to determine whether a spatial signal intensity distribution varies in a regular fashion -- and is therefore likely to be an image feature or image texture, or is highly random -- and likely to be noise. We present an outline of two possible methodologies for creating an ApEn-based noise filter: a modified median filter and a modified anisotropic diffusion scheme. We show that both approaches lead to effective noise reduction in MR images, with improved information-retaining properties when compared with their conventional counterparts. |
Year | Venue | Keywords |
|---|---|---|
1999 | IPMI | local measure,modified median filter,effective noise reduction,apen-based noise filter,mr image,computable measure,image texture,nonlinear smoothing,image feature,modified anisotropic diffusion scheme,signal intensity irregularity,image information,approximate entropy,noise reduction,median filter,anisotropic diffusion,image features |
Field | DocType | Volume |
Noise reduction,Anisotropic diffusion,Approximate entropy,Median filter,Sample entropy,Nonlinear system,Pattern recognition,Image texture,Computer science,Smoothing,Artificial intelligence | Conference | 1613 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-66167-0 | 4 |
PageRank | References | Authors |
0.65 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffrey J. M. Parker | 1 | 444 | 39.62 |
| Julia A Schnabel | 2 | 1978 | 151.49 |
| Gareth J Barker | 3 | 299 | 36.37 |