Name
Abstract | ||
|---|---|---|
Abstract Glaucoma is a chronic eye disease that causes blindness. It is the one of the most common causes of blindness in the world. It results in the loss of vision which cannot be regained. Although glaucoma is not curable, detection of the disease in proper time can stop its further progression. The optic disk (OD), optic cup (OC) and neuroretinal rim (NRR) constitute the important features in a retinal image that can be used to diagnose certain retinal diseases. The cup-to-disk ratio (CDR) and the shape of the NRR provides important indications for the diagnosis of glaucoma. In this paper, an approach for detection of glaucoma is presented based on CDR and ISNT rule. The use of two features enhances the classification accuracy. The OD and OC is segmented using the region growing method and watershed transformation. The proposed method is simple and computationally efficient and can be used in the computer-assisted diagnosis of glaucoma. The method has been tested on four publicly available databases (HRF, Messidor, DRIONS-DB, DIARETDB1) and images obtained from a local eye hospital (Sri Sankaradeva Netralaya). The proposed method achieves a sensitivity of 92.59 % in classifying glaucoma images, and an overall accuracy of 93.85 %. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s13721-015-0110-5 | NetMAHIB |
Keywords | Field | DocType |
CDR, Glaucoma, ISNT rule, Neuroretinal rim, Retinal image | Eye disease,Glaucoma,Biology,Retina,Ophthalmology,Retinal image,Optic disk,Artificial intelligence,Region growing,Retinal,Optic cup (anatomical),Machine learning | Journal |
Volume | Issue | ISSN |
5 | 3 | 2192-6670 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pranjal Das | 1 | 0 | 0.34 |
| S.R. Nirmala | 2 | 10 | 1.99 |
| Jyoti Prakash Medhi | 3 | 2 | 1.08 |