Name
Abstract | ||
|---|---|---|
Pathologies that occur on or near the fovea, such as clinically significant macular edema (CSME), represent high risk for vision loss. The presence of exudates, lipid residues of serous leakage from damaged capillaries, has been associated with CSME, in particular if they are located one optic disc-diameter away from the fovea. In this paper, we present an automatic system to detect exudates in the macula. Our approach uses optimal thresholding of instantaneous amplitude (IA) components that are extracted from multiple frequency scales to generate candidate exudate regions. For each candidate region, we extract color, shape, and texture features that are used for classification. Classification is performed using partial least squares (PLS). We tested the performance of the system on two different databases of 652 and 400 images. The system achieved an area under the receiver operator characteristic curve (AUC) of 0.96 for the combination of both databases and an AUC of 0.97 for each of them when they were evaluated independently. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JBHI.2013.2296399 | Biomedical and Health Informatics, IEEE Journal of |
Keywords | Field | DocType |
biomedical optical imaging,eye,feature extraction,image classification,image colour analysis,image segmentation,image texture,least squares approximations,medical image processing,optimisation,sensitivity analysis,vision,AUC,clinically significant macular edema,color feature extraction,damaged capillaries,exudate detection,fovea,instantaneous amplitude components,lipid residues,multiple frequency scales,multiscale optimization approach,optimal thresholding,partial least squares,receiver operator characteristic curve,shape feature extraction,texture feature extraction,vision loss,Amplitude-modulation frequency-modulation,clinically significant macular edema (CSME),diabetic retinopathy,partial least squares (PLS) | Computer vision,Receiver operating characteristic,Macular edema,Pattern recognition,Macula Lutea,Computer science,Image texture,Image segmentation,Feature extraction,Artificial intelligence,Thresholding,Contextual image classification | Journal |
Volume | Issue | ISSN |
18 | 4 | 2168-2194 |
Citations | PageRank | References |
12 | 0.74 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carla Agurto | 1 | 85 | 8.22 |
| V. Murray | 2 | 197 | 16.47 |
| Yu, H. | 3 | 21 | 2.36 |
| Wigdahl, J. | 4 | 12 | 0.74 |