Name
Abstract | ||
|---|---|---|
Automated chromosome classification has been an important pattern recognition problem for decades. Numerous attempts were made in the past to characterize chromosome band patterns as part of the feature description vector. In this paper we describe a recent study to employ wavelet packets as basis function sets to compute chromosome band pattern features. A total of 28 wavelet packet basis function sets were evaluated in this study. The experimental results are presented and compared with those currently best-performing method on two benchmark chromosome datasets |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/CBMS.2000.856898 | CBMS |
Keywords | Field | DocType |
wavelet-based band pattern descriptors,cellular biophysics,automated chromosome classification,pattern recognition,wavelet transforms,chromosome band pattern feature,chromosome band pattern,benchmark chromosome datasets,important pattern recognition problem,image classification,wavelet packet,basis function,best-performing method,recent study,wavelet packet basis function,medical image processing,cancer,digital images,wavelet packets | Data mining,Chromosome,Computer science,Basis function,Chromosome Band,Artificial intelligence,Contextual image classification,Wavelet packet decomposition,Wavelet transform,Wavelet,Computer vision,Pattern recognition,Network packet | Conference |
ISSN | ISBN | Citations |
1063-7125 | 0-7695-0484-1 | 10 |
PageRank | References | Authors |
1.30 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Wu | 1 | 26 | 3.34 |
| Kenneth R. Castleman | 2 | 91 | 12.80 |