Name
Abstract | ||
|---|---|---|
In this paper, we present an automated approach for Ultrasound Biomicroscopy (UBM) glaucoma images analysis. To increase the efficiency of the introduced approach, an intensity adjustment process is applied first using the Pulse Coupled Neural Network with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the anterior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Experimental results show that the introduced approach is very successful and has high detection accuracy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13577-4_44 | AST/UCMA/ISA/ACN |
Keywords | Field | DocType |
eye image,ultrasound biomicroscopy,ultrasound biomicroscopy glaucoma analysis,rough set analysis,rough set approach,neural network,pcnn-based segmentation algorithm,glaucoma clinical parameter,anterior chamber,glaucoma images analysis,automated approach,rough sets,ultrasound,image analysis,classification,rough set,median filter | Computer vision,Glaucoma,Reduct,Median filter,Normalization (statistics),Segmentation,Rough set,Ultrasound biomicroscopy,Artificial intelligence,Artificial neural network,Mathematics | Conference |
Volume | ISSN | ISBN |
6059 | 0302-9743 | 3-642-13576-5 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumya Banerjee | 1 | 116 | 29.41 |
| Hameed Al-Qaheri | 2 | 32 | 9.31 |
| El-Sayed A. El-Dahshan | 3 | 138 | 7.69 |
| Aboul Ella Hassanien | 4 | 1610 | 192.72 |