An Automatic Image Processing System for Glaucoma Screening
Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma’s popul...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2017/4826385 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850166958598651904 |
|---|---|
| author | Ahmed Almazroa Sami Alodhayb Kaamran Raahemifar Vasudevan Lakshminarayanan |
| author_facet | Ahmed Almazroa Sami Alodhayb Kaamran Raahemifar Vasudevan Lakshminarayanan |
| author_sort | Ahmed Almazroa |
| collection | DOAJ |
| description | Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma’s population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm’s accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm’s accuracy. The algorithm’s best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images. |
| format | Article |
| id | doaj-art-e1371f5abf1341fdab963610e163f930 |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-e1371f5abf1341fdab963610e163f9302025-08-20T02:21:17ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962017-01-01201710.1155/2017/48263854826385An Automatic Image Processing System for Glaucoma ScreeningAhmed Almazroa0Sami Alodhayb1Kaamran Raahemifar2Vasudevan Lakshminarayanan3Kellogg Eye Center, University of Michigan, 1000 Wall St, Ann Arbor, MI 48105, USABin Rushed Ophthalmic Center, King Fahd Branch Rd, Opposite King Fahad National Library, Al Olaya, Riyadh 12311, Saudi ArabiaDepartment of Electrical and Computer Engineering, Ryerson University, 350 Victoria St., Toronto, ON, M5B 2K3, CanadaSchool of Optometry and Vision Science, University of Waterloo, 200 Columbia St. W., Waterloo, ON, N2L 3G1, CanadaHorizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma’s population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm’s accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm’s accuracy. The algorithm’s best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images.http://dx.doi.org/10.1155/2017/4826385 |
| spellingShingle | Ahmed Almazroa Sami Alodhayb Kaamran Raahemifar Vasudevan Lakshminarayanan An Automatic Image Processing System for Glaucoma Screening International Journal of Biomedical Imaging |
| title | An Automatic Image Processing System for Glaucoma Screening |
| title_full | An Automatic Image Processing System for Glaucoma Screening |
| title_fullStr | An Automatic Image Processing System for Glaucoma Screening |
| title_full_unstemmed | An Automatic Image Processing System for Glaucoma Screening |
| title_short | An Automatic Image Processing System for Glaucoma Screening |
| title_sort | automatic image processing system for glaucoma screening |
| url | http://dx.doi.org/10.1155/2017/4826385 |
| work_keys_str_mv | AT ahmedalmazroa anautomaticimageprocessingsystemforglaucomascreening AT samialodhayb anautomaticimageprocessingsystemforglaucomascreening AT kaamranraahemifar anautomaticimageprocessingsystemforglaucomascreening AT vasudevanlakshminarayanan anautomaticimageprocessingsystemforglaucomascreening AT ahmedalmazroa automaticimageprocessingsystemforglaucomascreening AT samialodhayb automaticimageprocessingsystemforglaucomascreening AT kaamranraahemifar automaticimageprocessingsystemforglaucomascreening AT vasudevanlakshminarayanan automaticimageprocessingsystemforglaucomascreening |