scholarly journals A Hybrid Approach of Detection of Glaucoma with Optic Disk Segmentation and Microaneurysm Detection in Retinal Fundus Images

Author(s):  
N. Abisha ◽  
A. Lenin Fred ◽  
Shobhana ◽  
Ashwin G Singerji ◽  
2019 ◽  
Vol 13 (6) ◽  
pp. 1191-1198 ◽  
Author(s):  
Syed S. Naqvi ◽  
Nayab Fatima ◽  
Tariq M. Khan ◽  
Zaka Ur Rehman ◽  
M. Aurangzeb Khan

2019 ◽  
Vol 16 (1) ◽  
pp. 227-245 ◽  
Author(s):  
Maja Braovic ◽  
Darko Stipanicev ◽  
Ljiljana Seric

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.


Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 215
Author(s):  
Andrés Romero-Arellano ◽  
Ernesto Moya-Albor ◽  
Jorge Brieva ◽  
Ivan Cruz-Aceves ◽  
Juan Gabriel Avina-Cervantes ◽  
...  

In this work, a new medical image encryption/decryption algorithm was proposed. It is based on three main parts: the Jigsaw transform, Langton’s ant, and a novel way to add deterministic noise. The Jigsaw transform was used to hide visual information effectively, whereas Langton’s ant and the deterministic noise algorithm give a reliable and secure approach. As a case study, the proposal was applied to high-resolution retinal fundus images, where a zero mean square error was obtained between the original and decrypted image. The method performance has been proven through several testing methods, such as statistical analysis (histograms and correlation distributions), entropy computation, keyspace assessment, robustness to differential attack, and key sensitivity analysis, showing in each one a high security level. In addition, the method was compared against other works showing a competitive performance and highlighting with a large keyspace (>1×101,134,190.38). Besides, the method has demonstrated adequate handling of high-resolution images, obtaining entropy values between 7.999988 and 7.999989, an average Number of Pixel Change Rate (NPCR) of 99.5796%±0.000674, and a mean Uniform Average Change Intensity (UACI) of 33.4469%±0.00229. In addition, when there is a small change in the key, the method does not give additional information to decrypt the image.


2021 ◽  
Vol 1804 (1) ◽  
pp. 012128
Author(s):  
Ahmed Hashim Al-Sharfaa ◽  
Ali Yakoob Yousif ◽  
Enas Hamood Al-Saadi

Glaucoma is a autistic eye disease and major causes of firm blindness worldwide. For this we are trying to design a tool for early detection of glaucoma. In this paper glaucoma detection is based on the algorithm of retinal fundus images[1]. A supervised techniques for the detection of glaucoma is used. For the extraction of the features of the images we used PCA(principal component analysis). And for the classification support vectors are used. It shows mainly an artificial intelligent system for the segmentation of optic disk and cup. The accuracy of this model is comparatively much more greater than previously designed neural architectures


Author(s):  
Deepashree Devaraj ◽  
Prasanna Kumar S.C.

<p>Diabetic retinopathy (DR) is one of the driving reasons for visual deficiency, affecting people globally. Currently, the ophthalmologists need to inspect enormous number of images with a specific end goal to perform mass screening of Diabetic retinopathy. In this paper, an efficient Computer aided system based on a Hybrid framework is proposed for the early diagnosis of DR by extracting the early DR lesions such as microaneurysms and hemorrhages. The development of such a screening system would decrease the workload of the ophthalmologists, as they now need to look at those retinal images that are analyzed by the system, as irregularities. The retinal images obtained from standard retinal databases and Hospitals are pre-processed followed by the detection and elimination of blood vessels, optic disk and exudates. Quick propagation Neural Network is used for training and testing of the retinal fundus images since it has the fastest execution time. Linear Classification and Multi class classification of retinal fundus images are performed for the classification and grading of retinal fundus images into normal and abnormal using Alyuda Neuro-Intelligence software. A patient database is created using MySQL to store the required details of the patient and a graphical user interface is developed for an efficient usage of the system. The execution time of the system is found to be 7-9 seconds and is tested on 270 retinal fundus images. The precision and accuracy of the algorithm is 92.5% and 93.9%, respectively.</p>


2017 ◽  
Author(s):  
Javedkhan Y. Pathan ◽  
Dr.Pramod Patil

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