Name
Abstract | ||
|---|---|---|
The aim of this work is the development of an unsupervised method for the detection of the changes that occurred in multitemporal digital images of the fundus of the human retina, in terms of white and red spots. The images are acquired from the same patient at different times by a fundus camera. The proposed method is an unsupervised multiple classifier approach, based on a minimum-error thresholding technique. This technique is applied to separate the “change” and the “no-change” areas in a suitably defined difference image. In particular, the thresholding approach is applied to selected sub-images: the outputs of the different windows are combined with a majority vote approach, in order to cope with local illumination differences. A quantitative assessment of the change detection performances suggests that the proposed method is able to provide accurate change maps, although possibly affected by misregistration errors or calibration/acquisition artifacts. The comparison between the results obtained using the implemented multiple classifier approach and a standard one points out that the proposed algorithm provides an accurate detection of the temporal changes. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12127-2_10 | MCS |
Keywords | Field | DocType |
change detection performance,thresholding approach,multiple classifier approach,majority vote approach,retinal image,accurate change map,unsupervised multiple classifier approach,unsupervised change-detection,multiple-classifier approach,accurate detection,proposed algorithm,temporal change,majority voting,change detection,error threshold,digital image | Computer vision,Change detection,Pattern recognition,Computer science,Fundus (eye),Digital image,Artificial intelligence,Thresholding,Classifier (linguistics),Majority rule,Local illumination,Calibration | Conference |
Volume | ISSN | ISBN |
5997 | 0302-9743 | 3-642-12126-8 |
Citations | PageRank | References |
4 | 0.39 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giulia Troglio | 1 | 20 | 1.80 |
| Marina Alberti | 2 | 57 | 4.79 |
| Jón Atli Benediksson | 3 | 4 | 0.39 |
| Gabriele Moser | 4 | 919 | 76.92 |
| Serpico, S.B. | 5 | 560 | 48.52 |
| Einar Stefánsson | 6 | 4 | 0.73 |