Papilledema Detection in Fundus Images Using Hybrid Feature Set

Fatima, Khush Naseeb; Akram, M. Usman; Bazaz, Shafaat A.

doi:10.1109/icitcs.2015.7293007

Cited by 7 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is so because the AFIO data set contained images that were mostly of mild papilledema cases (grade 1, 2, 3), whereas in case of STARE the images were mostly of severe papilledema cases (grade 4, 5). Apart from this, we have extended our algorithm originally proposed in [7] to automatically diagnose and grade papilledema from digital retinal images and we have also compared our proposed system with the state of the art solution proposed in [6] on STARE data set and we obtained better performance in terms of accuracy as shown in Table 7. …”

Section: Overall Resultsmentioning

confidence: 99%

“…Apart from this, we have compared our proposed system with [6] on the same STARE data set and our proposed algorithm achieved better performance in terms of accuracy as shown in Table 6. In this paper, we have extended our proposed system originally proposed in [7] to incorporate efficient and early diagnosis of low grade papilledema from fundus scans. This has been done by adding a block in our original proposed system to automatically extract vascular features from the candidate retinal image.…”

Section: Discussionmentioning

confidence: 99%

“…Out of 100 images, 64 images were correctly classified into their respective grades of papilledema. Another method for classifying papilledema from fundus images employs 13 different features [7]. This method gives an accuracy of 96.67%, when applied to 30 images from the STARE data set.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs

Fatima¹,

Hassan²,

Akram³

et al. 2017

Biomed. Opt. Express

Self Cite

View full text Add to dashboard Cite

Rapid development in the field of ophthalmology has increased the demand of computer aided diagnosis of various eye diseases. Papilledema is an eye disease in which the optic disc of the eye is swelled due to an increase in intracranial pressure. This increased pressure can cause severe encephalic complications like abscess, tumors, meningitis or encephalitis, which may lead to a patient's death. Although there have been several papilledema case studies reported from a medical point of view, only a few researchers have presented automated algorithms for this problem. This paper presents a novel computer aided system which aims to automatically detect papilledema from fundus images. Firstly, the fundus images are preprocessed by going through optic disc detection and vessel segmentation. After preprocessing, a total of 26 different features are extracted to capture possible changes in the optic disc due to papilledema. These features are further divided into four categories based upon their color, textural, vascular and disc margin obscuration properties. The best features are then selected and combined to form a feature matrix that is used to distinguish between normal images and images with papilledema using the supervised support vector machine (SVM) classifier. The proposed method is tested on 160 fundus images obtained from two different data sets i.e. structured analysis of retina (STARE), which is a publicly available data set, and our local data set that has been acquired from the Armed Forces Institute of Ophthalmology (AFIO). The STARE data set contained 90 and our local data set contained 70 fundus images respectively. These annotations have been performed with the help of two ophthalmologists. We report detection accuracies of 95.6% for STARE, 87.4% for the local data set, and 85.9% for the combined STARE and local data sets. The proposed system is fast and robust in detecting papilledema from fundus images with promising results. This will aid physicians in clinical assessment of fundus images. It will not take away the role of physicians, but will rather help them in the time consuming process of screening fundus images.

show abstract

Section: Overall Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs

Fatima¹,

Hassan²,

Akram³

et al. 2017

Biomed. Opt. Express

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accordingly, in this study, we have introduced a method for detecting and segmenting OD in neonatal images. Furthermore, OD detection in adults is used to diagnose glaucoma [7] and papilloma [8]. An optic disk is a bright, circular area (Figure 1) where blood vessels converge.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Optic Disk Segmentation Approach from Retina of Neonates via Attention Based Deep Network

2022

IJE

View full text Add to dashboard Cite

Every year, many newborns lose their sight to retinopathy of prematurity (ROP) worldwide. Despite its high prevalence and adverse consequences, periodic examinations can effectively prevent it. The use of an intelligent system enables physicians to avoid medical mistakes while examining newborns. The optic disk (OD) is an integral part of the retina for grading the severity and progression of ROP. Due to the uneven brightness and lack of a defined OD border, the use of retinal images of infants is very challenging for OD diagnosis. This paper provides an innovative model of OD segmentation based on attention gate. Initially, the images were collected and preprocessed and inputted into a novel deep convolutional neural network consisting of attention in skip connections. The architecture is comprised of a two-stage convolutional network. Different outputs are obtained from two individual branches of the original image and image features in the first stage. The outputs were concatenated to transfer into the post-processing stage to identify the area related to the OD. The final results based on the Dice coefficient (Dice) and the Intersection-Over-Union (IoU) were 94.22% and 86.1%, respectively.

show abstract

Automatic detection of papilledema through fundus retinal images using deep learning

Saba

Akbar

Kolivand

et al. 2021

Microscopy Res & Technique

View full text Add to dashboard Cite

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.

show abstract

Papilledema Detection in Fundus Images Using Hybrid Feature Set

Cited by 7 publications

References 18 publications

Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs

Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs

An Automatic Optic Disk Segmentation Approach from Retina of Neonates via Attention Based Deep Network

Automatic detection of papilledema through fundus retinal images using deep learning

Contact Info

Product

Resources

About