scholarly journals Automatic Diabetic Retinopathy Diagnosis using Prewitt Edge Detection & Color Mapping from Fundus Imaging

2021 ◽  
Vol 10 (4) ◽  
pp. 19-23
Author(s):  
Megha Deshmukh ◽  
Vineeta Saxena Nigam
Keyword(s):  
Diabetic Retinopathy ◽  
Edge Detection ◽  
Blood Vessels ◽  
Rate System ◽  
Color Mapping ◽  
Fundus Imaging ◽  
Long Time ◽  
Mapping Techniques ◽  
High Level ◽  
Prewitt Edge Detection

Diabetic Retinopathy is a diabetic disease that directly affects the vision that causes damaged blood vessels at the back end of the eyes. It a complicated disease that cannot be recognized from normal eyes; a fundus imaging can reflect the impairments over the retina that causes partial or complete blindness that cannot be cured. It is mandatory for a routine examination that may lead to prevent from complete blindness because it can be prevented from current damaged blood vessels but it cannot be revert or treated. In the field of image processing; various diseases can be diagnosed automatically that saves humans life along with easiness for medical professionals. If a person pertains diabetes for a long time may have highest possibility for diabetic retinopathy. Here, the system has been proposed that can diagnose this disease with high level of accuracy with minimal false alarm rate. System uses Prewitt Edge Detection and Color Mapping techniques for recognizing diabetic retinopathy symptoms or damaged blood vessels from fundus imaging. Prewitt is highly sensitive for extracting impairments along with blood vessels and system is able to mask the unwanted area by using color correction tool.

Edge Detection on Light Field Images

2019 ◽  
pp. 174-197
Author(s):  
Yessaadi Sabrina ◽  
Laskri Mohamed Tayeb
Keyword(s):  
Edge Detection ◽  
Blood Vessels ◽  
Retinal Image ◽  
Light Field ◽  
Image Descriptors ◽  
Fundus Imaging ◽  
Novel Approach ◽  
Retinal Blood Vessels ◽  
Important Position ◽  
Direction Information

Digital fundus imaging is becoming an important task in computer-aided diagnosis and has gained an important position in the digital medical imaging domain. One of its applications is the retinal blood vessels extracting. Object detection in machine vision and image processing has gained increasing interest due to its social and security potential. Plenoptic imaging is a promising optical technique. This technique computes the location and the propagation direction information of the object light, which are used as efficient descriptors to detect and track the object displacement. In this chapter, the authors use an edge detection technique to extract and segment blood vessels in the retinal image. They propose a novel approach to detect vessels in a simulated light fields fundus image, based on the image representation with the first and the second order derivative, well known as gradient and Laplacian image descriptors. Since the difficulties to get a light field image of a fundus in the retinal image, the authors test their model in the image provided by Sha Tong et al.

scholarly journals PERBANDINGAN ALGORITMA CANNY EDGE DETECTION DAN PREWITT PADA DETEKSI STADIUM DIABETIK RETINOPATI

2021 ◽  
Vol 9 (02) ◽  
pp. 87-94
Author(s):  
Vina Ardelia Effendy ◽  
Febri Maspiyanti
Keyword(s):  
Diabetic Retinopathy ◽  
Edge Detection ◽  
Causes Of Death ◽  
Detection Method ◽  
Canny Edge Detection ◽  
Network Algorithms ◽  
A Value ◽  
Long Time ◽  
Canny Edge ◽  
Edge Detection Method

Diabetes is a serious threat to human health. In 2016, non-communicable diseases including Diabetes accounted for 70% of the total causes of death in the world. Diabetes if left unchecked will cause complications that can attack other organs to cause blindness called Diabetic Retinopathy (DR). Ophthalmologists make a grouping of diabetic characteristics of retinopathy by observing the retinal images of the eye taken using a fundus camera. This method requires a long time in the observation that allows errors in making observations, so image processing is needed to detect and classify the stage of diabetic retinopathy suffered by the patient. Thus, this research aims to help the process of early treatment of patients with diabetic retinopathy so as not to cause blindness. The data used in this study is DB0 Diaret data with a pixel size of 128 x 104 and the amount of data is 131. The methods used in this system include Canny Edge Detection, Prewitt, and stadium readings using Artificial Neural Network Algorithms. In this study the highest accuracy results obtained on the Canny Edge Detection method with a value of 90% while the Prewitt method has a 79% result. So, we get the conclusion that Canny Edge Detection is considered better.

To the memory of the geobotanist. Leonid E. Rodin (to the 100th anniversary of his birthday)

2007 ◽  
pp. 106-107
Author(s):  
B. K. Gannibal
Keyword(s):  
End Of Life ◽  
100Th Anniversary ◽  
State University ◽  
Botanical Institute ◽  
Komarov Botanical Institute ◽  
The Future ◽  
Long Time ◽  
High Level

Leonid Efimovich Rodin (1907-1990) was a graduate of Leningrad state University. To him, the future is known geobotanica, happened to a course in Botanical geography is still at the N. A. Bush. His teachers were also A. P. Shennikov and A. A. Korchagin, who subsequently headed related Department of geobotany and Botanical geography of Leningrad state University. This was the first school scientist. And since the beginning of the 30s of XX century and until the end of life L. E. was an employee of the Department of geobotany of the Komarov Botanical Institute (RAS), where long time worked together with E. M. Lavrenko, V. B. Sochava, B. A. Tikhomirov, V. D. Alexandrova and many other high-level professionals, first continuing to learn and gain experience, then defining the direction of development of geobotany in the Institute and the country as a whole.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals Analysis of Lipid Peroxidation by UPLC-MS/MS and Retinoprotective Effects of the Natural Polyphenol Pterostilbene

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 168
Author(s):  
Isabel Torres-Cuevas ◽  
Iván Millán ◽  
Miguel Asensi ◽  
Máximo Vento ◽  
Camille Oger ◽  
...  
Keyword(s):  
Lipid Peroxidation ◽  
Diabetic Retinopathy ◽  
Redox Homeostasis ◽  
Ultra Performance Liquid Chromatography ◽  
Protective Effects ◽  
Diabetic Retina ◽  
Key Factor ◽  
Adrenic Acid ◽  
Diabetic Rabbits ◽  
High Level

The loss of redox homeostasis induced by hyperglycemia is an early sign and key factor in the development of diabetic retinopathy. Due to the high level of long-chain polyunsaturated fatty acids, diabetic retina is highly susceptible to lipid peroxidation, source of pathophysiological alterations in diabetic retinopathy. Previous studies have shown that pterostilbene, a natural antioxidant polyphenol, is an effective therapy against diabetic retinopathy development, although its protective effects on lipid peroxidation are not well known. Plasma, urine and retinas from diabetic rabbits, control and diabetic rabbits treated daily with pterostilbene were analyzed. Lipid peroxidation was evaluated through the determination of derivatives from arachidonic, adrenic and docosahexaenoic acids by ultra-performance liquid chromatography coupled with tandem mass spectrometry. Diabetes increased lipid peroxidation in retina, plasma and urine samples and pterostilbene treatment restored control values, showing its ability to prevent early and main alterations in the development of diabetic retinopathy. Through our study, we are able to propose the use of a derivative of adrenic acid, 17(RS)-10-epi-SC-Δ15-11-dihomo-IsoF, for the first time, as a suitable biomarker of diabetic retinopathy in plasmas or urine.

scholarly journals Retinal blood vessels segmentation using classical edge detection filters and the neural network

2021 ◽  
Vol 23 ◽  
pp. 100521
Author(s):  
Beaudelaire Saha Tchinda ◽  
Daniel Tchiotsop ◽  
Michel Noubom ◽  
Valerie Louis-Dorr ◽  
Didier Wolf
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Blood Vessels ◽  
The Neural Network ◽  
Retinal Blood Vessels

scholarly journals Corticobasal Degeneration Misdiagnosed As Musculoskeletal Disease: A Challenging Diagnosis of Higher-Level Gait Disease

2020 ◽  
Vol 11 (04) ◽  
pp. 640-642
Author(s):  
Halil Onder
Keyword(s):  
Corticobasal Degeneration ◽  
The Elderly ◽  
Gait Disorders ◽  
Physiological Changes ◽  
Gait Abnormality ◽  
Age Related ◽  
Musculoskeletal Problems ◽  
Neurological Exam ◽  
Long Time ◽  
High Level

AbstractGait disorders are common in the elderly as there are various causes of neurological and non-neurological conditions. On the other hand, most of the gait parameters do change with advancing age which is identified as age-related physiological changes in gait. At this point, the discrimination between age-related physiological changes and gait disorders may be strictly challenging. After identifying gait as an abnormal pattern, classification of it and making the responsible pathophysiology also require high-level expertise in this regard. Herein, we present a rare patient with corticobasal degeneration (CBD) who had admitted initially due to complaints of gait problems. Over a long time, the patient had received the misdiagnosis of gait abnormality due to musculoskeletal problems by multiple physicians. However, the detailed neurological exam showed a higher level gait disorder (HLGD). Further investigations at this point yielded the diagnosis of CBD.

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

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