Name
Title | ||
|---|---|---|
Detection of Retinal Nerve Fiber Layer Defects in Retinal Fundus Images using Gabor Filtering |
Abstract | ||
|---|---|---|
Retinal nerve fiber layer defect (NFLD) is one of the most important findings for the diagnosis of glaucoma reported by ophthalmologists. However, such changes could be overlooked, especially in mass screenings, because ophthalmologists have limited time to search for a number of different changes for the diagnosis of various diseases such as diabetes, hypertension and glaucoma. Therefore, the use of a computer-aided detection (CAD) system can improve the results of diagnosis. In this work, a technique for the detection of NFLDs in retinal fundus images is proposed. In the preprocessing step, blood vessels are "erased" from the original retinal fundus image by using morphological filtering. The preprocessed image is then transformed into a rectangular array. NFLD regions are observed as vertical dark bands in the transformed image. Gabor filtering is then applied to enhance the vertical dark bands. False positives (FPs) are reduced by a rule-based method which uses the information of the location and the width of each candidate region. The detected regions are back-transformed into the original configuration. In this preliminary study, 71% of NFLD regions are detected with average number of FPs of 3.2 per image. In conclusion, we have developed a technique for the detection of NFLDs in retinal fundus images. Promising results have been obtained in this initial study. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1117/12.710181 | Proceedings of SPIE |
Keywords | Field | DocType |
computer-aided detection (CAD),fundus images,retinal nerve fiber layer defects,Gabor filtering | Computer vision,Glaucoma,Nerve fiber layer,Computer-aided diagnosis,Fundus (eye),Filter (signal processing),Preprocessor,Artificial intelligence,Retinal,Rectangular array,Medicine | Conference |
Volume | ISSN | Citations |
6514 | 0277-786X | 8 |
PageRank | References | Authors |
1.14 | 2 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshinori Hayashi | 1 | 40 | 5.10 |
| Toshiaki Nakagawa | 2 | 77 | 7.38 |
| Y Hatanaka | 3 | 276 | 24.77 |
| Akira Aoyama | 4 | 8 | 1.48 |
| Masakatsu Kakogawa | 5 | 12 | 2.34 |
| Takeshi Hara | 6 | 639 | 79.10 |
| Hiroshi Fujita | 7 | 8 | 1.14 |
| Tetsuya Yamamoto | 8 | 108 | 20.79 |