Name
Abstract | ||
|---|---|---|
Two approaches to the characterization of three-dimensional (3-D) textures are presented: one based on gradient vectors and one on generalized co-occurrence matrices. They are investigated with the help of simulated data for their behavior in the presence of noise and for various values of the parameters they depend on. They are also applied to several medical volume images characterized by the presence of microtextures and their potential as diagnostic tools and tools for quantifying and monitoring the progress of various pathologies is discussed. No firm medical conclusions can be drawn as not enough clinical data are available. The gradient based method appears to be more appropriate for the characterization of microtextures. It also shows more consistent behavior as a descriptor of pathologies than the generalized co-occurrence matrix approach |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/83.748890 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
mathematics,medical simulation,noise,feature extraction,three dimensional,indexing terms,biomedical imaging,image texture,image analysis,image registration,pathology,co occurrence matrix,patient monitoring,anisotropic magnetoresistance | Gradient method,Computer vision,Medical simulation,Pattern recognition,Image texture,Matrix (mathematics),Computer science,Medical imaging,Image processing,Feature extraction,Artificial intelligence,Image registration | Journal |
Volume | Issue | ISSN |
8 | 3 | 1057-7149 |
Citations | PageRank | References |
23 | 2.06 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassili A. Kovalev | 1 | 23 | 2.06 |
| Maria Petrou | 2 | 2270 | 223.83 |
| Yaroslav S. Bondar | 3 | 23 | 2.06 |