scholarly journals An Automatic Image Processing System for Glaucoma Screening

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Ahmed Almazroa ◽  
Sami Alodhayb ◽  
Kaamran Raahemifar ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Processing System ◽  
Fundus Images ◽  
Type Ii ◽  
Fuzzy Approach ◽  
Glaucoma Screening ◽  
Automatic Image Processing ◽  
Retinal Fundus Images ◽  
Retinal Fundus

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.

scholarly journals Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images

2020 ◽  
Vol 10 (11) ◽  
pp. 3833 ◽  
Author(s):  
Haidar Almubarak ◽  
Yakoub Bazi ◽  
Naif Alajlan
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Optic Nerve Head ◽  
Fine Tuning ◽  
Fundus Images ◽  
Two Stage ◽  
Second Stage ◽  
Retinal Fundus Images ◽  
Tuning Strategy ◽  
Retinal Fundus

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.

scholarly journals A Wide-Ranging Review on Diabetic Retinotherapy

2020 ◽  
Vol 18 (02) ◽  
pp. 169-187
Author(s):  
Toufique Ahmed Soomro ◽  
Ahsin Murtaza Bughio ◽  
Shahid Hussain Siyal ◽  
Ali Anwar Panwar ◽  
Nasreen Nizamani
Keyword(s):  
Image Processing ◽  
Blood Vessels ◽  
Detection System ◽  
Eye Diseases ◽  
Fundus Images ◽  
The Past ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Computerized System ◽  
Automatic Algorithm

Diabetic Retinopathy (DR) is one of the major eye diseases that causes damage to retina of the human eye ball due to the rupture of tiny blood vessels. DR is identified by the ophthalmologists on the basis of various specifications i.e., textures, blood vessels and pathologies. The ophthalmologists are recently considering software for eye diseases detection based on image processing designed by the computing techniques and bio-medical images. In the analysis of medical imaging, traditional techniques of image processing and computer vision have played an important role in the field of ophthalmology. From the past two decades, there is a tremendous advancement in the development of computerized system for DR detection. This paper comprises the five parts of analysis on image based retinal detection DR, named as review of low varying contrast techniques of the retinal fundus Images (RFI), review of noise effect in the fundus images, review of pathology detection method from the retinal fundus images, review of blood vessels extraction from the RFI, and review of automatic algorithm for the DR detection. This paper presents a comprehensive detail to each problem in the retinal images. The procedures that are currently utilized to analyze the contrast issue and noise issues are discussed in detail. The paper also explains the techniques used for segmentation. In the end, the recent automated detection system of related eye diseases or DR is described.

scholarly journals Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 8527-8541 ◽  
Author(s):  
Jiapan Guo ◽  
George Azzopardi ◽  
Chenyu Shi ◽  
Nomdo M. Jansonius ◽  
Nicolai Petkov
Keyword(s):  
Fundus Images ◽  
Automatic Determination ◽  
Glaucoma Screening ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Cup To Disc Ratio

scholarly journals Automated Glaucoma Screening in Retinal Fundus Images

2015 ◽  
Vol 10 (9) ◽  
pp. 129-136 ◽  
Author(s):  
Srinivasan Aruchamy ◽  
Partha Bhattacharjee ◽  
Goutam Sanyal
Keyword(s):  
Fundus Images ◽  
Glaucoma Screening ◽  
Retinal Fundus Images ◽  
Retinal Fundus
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