Name
Abstract | ||
|---|---|---|
This paper presents a combined approach to automatic extraction of blood vessels in retinal images. The proposed procedure is composed of two phases: a wavelet transform-based preprocessing phase and a NN-based one. Several neural net topologies and training algorithms are considered with the aim of selecting an effective combined method. Human retinal fundus images, derived from the publicly available ophthalmic database DRIVE, are processed to detect retinal vessels. The approach is tested by considering performances in terms of sensitivity and specificity values obtained from vessel classification. The quality of vessel identifications is evaluated on obtained image by computing both sensitivity values and specificity ones and by relating them in ROC curves. A comparison of performances by ROC curve areas for various methods is reported. |
Year | Venue | Keywords |
|---|---|---|
2009 | ICIC (2) | retinal vessel,retinal image,human retinal fundus image,sensitivity value,retinal vessel extraction,effective combined method,roc curve,combined neural network-wavelet enhancement,specificity value,combined approach,roc curve area,blood vessel,neural net,artificial neural network,neural network,wavelet transform |
Field | DocType | Volume |
Computer vision,Receiver operating characteristic,Pattern recognition,Computer science,Fundus (eye),Preprocessor,Artificial intelligence,Retinal,Wavelet filter,Artificial neural network,Wavelet transform,Wavelet | Conference | 5755 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-04019-5 | 7 |
PageRank | References | Authors |
0.55 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonarda Carnimeo | 1 | 38 | 7.57 |
| Vitoantonio Bevilacqua | 2 | 468 | 66.40 |
| Lucia Cariello | 3 | 32 | 2.88 |
| G. Mastronardi | 4 | 167 | 26.29 |