COMPUTER HYBRID SYSTEM OF HEMORRHAGE (HES) DETECTION USED FOR AIDED DIAGNOSIS OF DIABETIC RETINOPATHY

2021 ◽  
pp. 2150044
Author(s):  
A. FEROUI ◽  
M. MESSADI ◽  
A. LAZOUNI ◽  
A. BESSAID
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Vitreous Hemorrhage ◽  
Unsupervised Classification ◽  
Fundus Images ◽  
Computationally Efficient ◽  
Physiological Abnormalities ◽  
Lesion Level ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Diabetes cause’s metabolic and physiological abnormalities in the retina and the changes suggest a role for inflammation in the development of diabetic retinopathy. Abnormal blood vessels can form in the back of the eye of a person with diabetes. These new blood vessels are weaker and prone to breaking and causing hemorrhage (HEs). Diabetic retinopathy (DR) accounts for 31.5–54% of all cases of vitreous hemorrhage in adults in the world. Therefore, detection of HEs is still a challenging factor task for computer-aided diagnostics of DR. Many researchers have developed advanced algorithms of hemorrhages detection using fundus images. In this paper, a robust and computationally efficient approach for HEs with different shape and size detection and classification is presented. First, brightness correction and contrast enhancement are applied to fundus images. Second, candidate hemorrhages are extracted by using an unsupervised classification algorithm. Third, an approach based on mathematical morphology is carried out for vascular network and macula segmentation. Finally, a total of 13 HEs features are considered in this study and selected for classification. The proposed method is evaluated on 419 fundus images of DIARETDB0, DIARETDB1 and MESSIDOR databases. Experimental results show that overall average sensitivity, specificity, predictive value and accuracy for hemorrhage in lesion level are 98.90%, 99.66%, 97.63% and 99.56%, respectively. The results show that the proposed method outperforms other state-of-the-art methods in detection of hemorrhages. These results indicate that this new method may improve the performance of diagnosis of DR system.

scholarly journals A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks

2020 ◽  
Vol 10 (4) ◽  
pp. 5986-5991
Author(s):  
A. N. Saeed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Performance ◽  
Fundus Images ◽  
Retinal Blood Vessels

Artificial Intelligence (AI) based Machine Learning (ML) is gaining more attention from researchers. In ophthalmology, ML has been applied to fundus photographs, achieving robust classification performance in the detection of diseases such as diabetic retinopathy, retinopathy of prematurity, etc. The detection and extraction of blood vessels in the retina is an essential part of various diagnosing problems associated with eyes, such as diabetic retinopathy. This paper proposes a novel machine learning approach to segment the retinal blood vessels from eye fundus images using a combination of color features, texture features, and Back Propagation Neural Networks (BPNN). The proposed method comprises of two steps, namely the color texture feature extraction and training the BPNN to get the segmented retinal nerves. Magenta color and correlation-texture features are given as input to the BPNN. The system was trained and tested in retinal fundus images taken from two distinct databases. The average sensitivity, specificity, and accuracy obtained for the segmentation of retinal blood vessels were 0.470%, 0.914%, and 0.903% respectively. Results obtained reveal that the proposed methodology is excellent in automated segmentation retinal nerves. The proposed segmentation methodology was able to obtain comparable accuracy with other methods.

Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images

2010 ◽  
Vol 1 (3) ◽  
pp. 16-27 ◽  
Author(s):  
I. K. E. Purnama ◽  
K. Y. E. Aryanto ◽  
M. H. F. Wilkinson
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Human Vision ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Pathological Changes ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Filtering Approach

Retinal blood vessels can give information about abnormalities or disease by examining its pathological changes. One abnormality is diabetic retinopathy, characterized by a disorder of retinal blood vessels resulting from diabetes mellitus. Currently, diabetic retinopathy is one of the major causes of human vision abnormalities and blindness. Hence, early detection can lead to proper treatment, and segmentation of the abnormality provides a map of retinal vessels that can facilitate the assessment of the characteristics of these vessels. In this paper, the authors propose a new method, consisting of a sequence of procedures, to segment blood vessels in a retinal image. In the method, attribute filtering with a so-called Max-Tree is used to represent the image based on its gray value. The filtering process is done using the branches filtering approach in which the tree branches are selected based on the non-compactness of the nodes. The selection is started from the leaves. This experiment was performed on 40 retinal images, and utilized the manual segmentation created by an observer to validate the results. The proposed method can deliver an average accuracy of 94.21%.

scholarly journals Removal of False Blood Vessels Using Shape Based Features and Image Inpainting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Amna Waheed ◽  
Zahra Waheed ◽  
M. Usman Akram ◽  
Arslan Shaukat
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Image Inpainting ◽  
Human Retina ◽  
Ocular Diseases ◽  
Vascular Disorder ◽  
Automated Quantification ◽  
Vessel Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Automated quantification of blood vessels in human retina is the fundamental step in designing any computer-aided diagnosis system for ophthalmic disorders. Detection and analysis of variations in blood vessels can be used to diagnose several ocular diseases like diabetic retinopathy. Diabetic Retinopathy is a progressive vascular disorder caused due to variations in blood vessels of retina. These variations bring different abnormalities like lesions, exudates, and hemorrhages in human retina which make the vessel detection problematic. Therefore, automated retinal analysis is required to cater the effect of lesions while segmenting blood vessels. The proposed framework presents two improved approaches to carry out vessel segmentation in the presence of lesions. The paper mainly aims to extract true vessels by reducing the effect of abnormal structures significantly. First method is a supervised approach which extracts true vessels by performing region based analysis of retinal image, while second method intends to remove lesions before extracting blood vessels by using an inpainting technique. Both methods are evaluated on STARE and DRIVE and on our own database AFIO. Experimental results demonstrate the excellence of the proposed system.

The Diagnosis Of Diabetic Retinopathy By Means Of Transfer Learning And Fine-Tuned Dense Layer Pipeline

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 68-72
Author(s):  
Abdulaziz Abdo Salman ◽  
Ismail Mohd Khairuddin ◽  
Anwar P.P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman
Keyword(s):  
Blood Flow ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Transfer Learning ◽  
The Body ◽  
Dense Layer ◽  
Fundus Images ◽  
Detection Approach ◽  
Deep Learning Model

Diabetes is a global disease that occurs when the body is disabled pancreas to secrete insulin to convert the sugar to power in the blood. As a result, some tiny blood vessels on the part of the body, such as the eyes, are affected by high sugar and cause blocking blood flow in the vessels, which is called diabetic retinopathy.  This disease may lead to permanent blindness due to the growth of new vessels in the back of the retina causing it to detach from the eyes. In 2016, 387 million people were diagnosed with Diabetic retinopathy, and the number is growing yearly, and the old detection approach becomes worse. Therefore, the purpose of this paper is to computerize the old method of detecting different classes of DR from 0-4 according to severity by given fundus images. The method is to construct a fine-tuned deep learning model based on transfer learning with dense layers. The used models here are InceptionV3, VGG16, and ResNet50 with a sharpening filter. Subsequently, InceptionV3 has achieved 94% as the highest accuracy among other models.  

Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images

2012 ◽  
pp. 208-218
Author(s):  
I. K. E. Purnama ◽  
K. Y. E. Aryanto ◽  
M. H. F. Wilkinson
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Human Vision ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Pathological Changes ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Filtering Approach

Retinal blood vessels can give information about abnormalities or disease by examining its pathological changes. One abnormality is diabetic retinopathy, characterized by a disorder of retinal blood vessels resulting from diabetes mellitus. Currently, diabetic retinopathy is one of the major causes of human vision abnormalities and blindness. Hence, early detection can lead to proper treatment, and segmentation of the abnormality provides a map of retinal vessels that can facilitate the assessment of the characteristics of these vessels. In this paper, the authors propose a new method, consisting of a sequence of procedures, to segment blood vessels in a retinal image. In the method, attribute filtering with a so-called Max-Tree is used to represent the image based on its gray value. The filtering process is done using the branches filtering approach in which the tree branches are selected based on the non-compactness of the nodes. The selection is started from the leaves. This experiment was performed on 40 retinal images, and utilized the manual segmentation created by an observer to validate the results. The proposed method can deliver an average accuracy of 94.21%.

Semi-Automated Computer Analysis of Retinal Vessels in Premature Infants

2012 ◽  
Vol 241-244 ◽  
pp. 2962-2968
Author(s):  
Rashmi Turior ◽  
Pornthep Chutinantvaron ◽  
Bunyarit Uyyanonvara
Keyword(s):  
Blood Vessels ◽  
Computer Assisted ◽  
Fundus Images ◽  
Single Image ◽  
Quantitative Measurements ◽  
Comprehensive Information ◽  
Retinal Blood Vessels ◽  
Length Width ◽  
Almost All ◽  
Sensitivity Specificity

Almost all ocular and systemic diseases affect blood vessel attributes (tortuosity, length, width, and curvature). Quantitative measurements of these attributes could thus provide useful tool for diagnosing the severity of several diseases. However, it is still unclear how best to represent the attribute values of multiple vessels in a single image. Graphical user interface (GUI) is a promising step towards the development of a semi-automated computer assisted tool. The objective of this study is to develop a GUI for effective observation and robust retinal blood vessels analysis by ophthalmologists and to comprehend the distribution of vessels attributes. Blood vessels from 45 digital fundus images of infant retina are extracted, its centerline is delineated and tortuosity is analyzed from different putative and proposed techniques to provide reliable and comprehensive information for the retinal vasculature. K means clustering technique is used for classification analysis of different tortuosity metrics and its performance is evaluated based on sensitivity, specificity, and accuracy. The results are validated by comparing with expert ophthalmologists’ ground truths. Among the various proposed tortuosity metrics, one of our tortuosity indexes attains the highest classification accuracy of 91.42% with sensitivity and specificity of 86.36% and 97.82% respectively.

scholarly journals Diabetic retinopathy classification using deep convolutional neural network

2021 ◽  
Vol 24 (1) ◽  
pp. 208
Author(s):  
Akshita L. ◽  
Harshul Singhal ◽  
Ishita Dwivedi ◽  
Poonam Ghuli
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Traditional Method ◽  
High Accuracy ◽  
Vision Impairment ◽  
Fundus Images ◽  
Computationally Efficient ◽  
Deep Convolutional Neural Networks ◽  
Deep Convolution Neural Network ◽  
Different Levels

<span>Diabetic retinopathy (DR) is a diabetic impairment that affects the eyes and if not treated could lead to permanent vision impairment. Traditionally, Ophthalmologists perform diagnosis of DR by checking for existence and any seriousness of some subtle features in the fundus images. This process is not very efficient as it takes a lot of time and resources. DR testing of all the patients, a lot of which are undiagnosed or untreated, is a big task due to the inefficiency of the traditional method. This paper was written with the aim to propose a classification system based on an efficient deep convolution neural network (DCNN) model which is computationally efficient. Amongst other supervised algorithms involved, proposed solution is to find a way to efficiently classify the fundus images into 5 different levels of severity. Application of segmentation after the pre-processing and then use of deep convolutional neural networks on the dataset results in a high accuracy of 91.52%. The result achieved is high given the limitations of the dataset and computational powers.</span>

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