scholarly journals Vision Atrophy Screening and Revelation

2021 ◽  
Author(s):  
Kiruthiga Devi M ◽  
Lingamuthu K ◽  
Baskar M ◽  
Deepa B ◽  
Merlin G
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Low Cost ◽  
Remote Diagnostics ◽  
Glaucoma Detection ◽  
Screening Services ◽  
Detection And Diagnosis ◽  
Retinal Fundus ◽  
To Receive

Glaucoma which is known as the “thief of sight”, is an irreversible eye disease It is mainly caused by increased intraocular pressure (IOP), or loss of blood supply to the optic nerve. Glaucoma detection and diagnosis is very important. By analyzing the optic disc and its surroundings, This paper introduces a method for providing automated glaucoma screening services based on a framework that proposes a retinal image synthesizer for glaucoma assessment by analyzing the optic disc and its surroundings. The Cup to Disc Ratio (CDR) is critical for the system, and it is calculated using 2-D retinal fundus images. The synthetic images produced by our system are compared quantitatively. The structural properties of synthetic and real images are analyzed, and the quality of colour is calculated by extracting the 2-D histogram. The system allows patients to receive low-cost remote diagnostics from a distance, preventing blindness and vision loss by early detection and management.

The Costs and Benefits of Shareholder Democracy: Gadflies and Low-Cost Activism

2020 ◽  
Author(s):  
Nickolay Gantchev ◽  
Mariassunta Giannetti
Keyword(s):  
Low Cost ◽  
Shareholder Value ◽  
Career Concerns ◽  
Costs And Benefits ◽  
Cross Sectional ◽  
Shareholder Proposals ◽  
Active Individual ◽  
Shareholder Democracy ◽  
To Receive

Abstract We show that there is cross-sectional variation in the quality of shareholder proposals. On average, proposals submitted by the most active individual sponsors are less likely to receive majority support, but they occasionally pass if shareholders mistakenly support them and may even be implemented due to directors’ career concerns. While gadfly proposals destroy shareholder value if they pass, shareholder proposals on average are value enhancing in firms with more informed shareholders. We conclude that more informed voting could increase the benefits associated with shareholder proposals.

Monitoring the Progressing Glaucoma Patient – What Are the Challenges?

2009 ◽  
Vol 03 (02) ◽  
pp. 23
Author(s):  
Karolien Termote ◽  
Thierry Zeyen ◽  
◽  
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Glaucoma Patient ◽  
Field Testing ◽  
Rate Of Progression ◽  
Progression Of Disease ◽  
The Individual ◽  
And Function ◽  
The Impact

Diagnosing glaucoma progression is complex. It is essential to assess both structure and function to detect progression. Establishing a reliable baseline is crucial in this process. A functional baseline requires repeated visual field testing. Documentation of the optic disc appearance is necessary for the acquisition of the structural baseline. This can be achieved by the complementary modes of both clinical and imaging devicebased optic disc documentation. Imaging-based methods to assess progression and rate of progression are likely to prove important in the future, but currently more guidance for their use in clinical practice is required. Rate of progression provides important information about the risk of vision loss. Guidelines therefore recommend determining the rate of progression for the individual patient when planning management. Adherence issues need to be addressed before changing treatment strategy, since poor compliance may play a considerable role in the progression of disease in many patients. In conclusion, we must strive to improve the management of glaucoma to limit the impact disease progression has on the patient’s quality of life.

scholarly journals Glaucoma detection using cup to disc ratio and artificial neural networks

2017 ◽  
Vol 7 (1.5) ◽  
pp. 135 ◽  
Author(s):  
Gayathri R. ◽  
Rao P. V.
Keyword(s):  
Early Stage ◽  
Eye Disease ◽  
Human Beings ◽  
Eye Infections ◽  
Glaucoma Detection ◽  
Artificial Neural ◽  
Handling Methods ◽  
Retinal Fundus ◽  
Cup To Disc Ratio

Now-a-days, the most commonly predicted eye disease in human beings is glaucoma; loss of vision gradually may turn into blindness. Advanced image handling methods empower osteopathic specialist to distinguish and treat a few eye infections like diabetic retinopathy and glaucoma. The pressure in the optic nerve of the eye may lead to get affected by glaucoma, which is most regular reason for visual deficiency of the peoples, if not treated appropriately at early stage. The main objective of this paper is the detection of glaucoma and classifies the disease based on its severity using artificial neural network. In this paper mainly focused on pre -processing of retinal fundus images for improving the quality of detection and easy to further handling. The simulation results to obtain using MATLAB for the better accuracy in detecting glaucoma for abnormality using Cup to Disc ratio of retinal fund us images. 

scholarly journals NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

2017 ◽  
Vol 10 (03) ◽  
pp. 1750007 ◽  
Author(s):  
Umarani Balakrishnan
Keyword(s):  
Optic Disc ◽  
Retinal Image ◽  
Automatic Segmentation ◽  
Intensity Variation ◽  
Glaucoma Detection ◽  
Roi Extraction ◽  
Ocular Pressure ◽  
Sensitivity Specificity ◽  
The Given

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure (IOP), which damages the vision of eyes. So, detecting and classifying Glaucoma is an important and demanding task in recent days. For this purpose, some of the clustering and segmentation techniques are proposed in the existing works. But, it has some drawbacks that include inefficient, inaccurate and estimates only the affected area. In order to solve these issues, a Neighboring Differential Clustering (NDC) — Intensity Variation Masking (IVM) are proposed in this paper. The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc. This work includes three stages such as, preprocessing, clustering and segmentation. At first, the given retinal image is preprocessed by using the Gaussian Mask Updated (GMU) model for eliminating the noise and improving the quality of the image. Then, the cluster is formed by extracting the threshold and patterns with the help of NDC technique. In the segmentation stage, the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method. Here, the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification. In experiments, the results of both existing and proposed techniques are evaluated in terms of sensitivity, specificity, accuracy, Hausdorff distance, Jaccard and dice metrics.

scholarly journals Detection of Glaucoma in Retinal Fundus Images using Fast Fuzzy C Mean Clustering

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Fluid Pressure ◽  
Fundus Images ◽  
New Approach ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Glaucoma is one of the major causes of vision loss in today’s world. Glaucoma is a disease in the eye where fluid pressure in the eye increases; if it is not timely cured, the patient may lose their vision. Glaucoma can be detected by examining boundary of optics cup and optics disc acquired from fundus images. The proposed method suggest automatic detect the boundary of optics cup and optics disc with processing of fundus images. This paper explores the new approach fast fuzzy C-mean technique for segmenting the optic disc and optic cup in fundus images. Results evaluated by fast fuzzy C mean a technique is faster than fuzzy C-mean method. The proposed method reported results to 91.91%, 90.49% and 90.17% when tested on DRIONS, DRIVE and STARE on publicly available databases of fundus images.

scholarly journals Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

2021 ◽  
Author(s):  
Mohammed Yousef Salem Ali ◽  
Mohamed Abdel-Nasser ◽  
Mohammed Jabreel ◽  
Aida Valls ◽  
Marc Baget
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Weighted Averaging ◽  
Fundus Images ◽  
Owa Operators ◽  
Detection Systems ◽  
Glaucoma Detection

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

scholarly journals An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 434
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Ali Javed ◽  
Usman Tariq ◽  
Hwan-Seung Yong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Image Database ◽  
Eye Color ◽  
Glaucoma Screening ◽  
Glaucoma Detection ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Screening Procedures

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.

scholarly journals Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

2016 ◽  
Author(s):  
Maíla De Lima Claro ◽  
Leonardo De Moura Santos ◽  
Wallinson Lima e Silva ◽  
Flávio Henrique Duarte De Araújo ◽  
Nayara Holanda De Moura ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optic Disc ◽  
Vision Loss ◽  
Detection Method ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Medical Settings ◽  
Glaucoma Detection ◽  
Different Color ◽  
Processing Techniques

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

scholarly journals Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

2020 ◽  
Vol 11 (2) ◽  
pp. 111-120
Author(s):  
Dunja Božić-Štulić ◽  
Maja Braović ◽  
Darko Stipaničev
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Contextual Information ◽  
Automatic Detection ◽  
Semantic Segmentation ◽  
Fundus Images ◽  
Glaucoma Detection ◽  
Average Accuracy ◽  
Optic Cup ◽  
Retinal Fundus

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75%

scholarly journals Problematic issues relating to the quality of information used for accounting and management accounting of low-cost assets

2018 ◽  
pp. 115-124 ◽  
Author(s):  
Zenovii Zadorozhnyi
Keyword(s):  
Intangible Assets ◽  
Low Cost ◽  
Research Practice ◽  
Financial Health ◽  
Quality Of Information ◽  
Tangible Assets ◽  
Operation Period ◽  
One Year ◽  
To Receive

The article presents an analysis of research practice on the classification criteria of current assets, noncurrent assets and low-cost assets. It is proved that the main feature for dividing assets into current and noncurrent (capital) ones should be seen in their planning operation period. It is reasoned that low-cost assets include assets worth up to UAH 2,500. It is proposed to change the name of Account 22 “Low-cost items” to “Non-durables” and to consolidate there its subsidiary accounts, respectively, “expensive”, “cheap” and “low-cost” non-durable items. Working clothes, safety footwear, and tools, whose planning operation period exceeds one year, should be attributed as noncurrent assets and presented on Account 10 “Capital assets” and Account 11 “Other noncurrent tangible assets”. The necessity of reducing primary documentation for accounting durable items is proved. It is substantiated that accounting treatment of intangible assets should be carried out not only as part of noncurrent assets on Account 12 “Intangible assets”, but also as part of current assets on Account 29 “Current intangible assets”. It is shown that the proposed changes will give internal users and investors an opportunity to receive more transparent and reliable information about enterprise’s financial health.

Sign in / Sign up

Export Citation Format

Share Document