Blood vessels extraction and classification into arteries and veins in retinal images

2013 ◽  
Author(s):  
Jihene Malek ◽  
Rached Tourki
Keyword(s):  
Blood Vessels ◽  
Retinal Images ◽  
Arteries And Veins

Automated characterization of blood vessels as arteries and veins in retinal images

2013 ◽  
Vol 37 (7-8) ◽  
pp. 607-617 ◽  
Author(s):  
Qazaleh Mirsharif ◽  
Farshad Tajeripour ◽  
Hamidreza Pourreza
Keyword(s):  
Blood Vessels ◽  
Retinal Images ◽  
Arteries And Veins

scholarly journals Improving Efficiency in Separating Blood Vessels from Retinal Images with Deep Learning Techniques

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3637-3640
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Blood Vessels ◽  
Age Related Macular Degeneration ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Age Related ◽  
Learning Techniques ◽  
Retinal Pathologies

Retinal vessels ID means to isolate the distinctive retinal configuration issues, either wide or restricted from fundus picture foundation, for example, optic circle, macula, and unusual sores. Retinal vessels recognizable proof investigations are drawing in increasingly more consideration today because of pivotal data contained in structure which is helpful for the identification and analysis of an assortment of retinal pathologies included yet not restricted to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the advancement of right around two decades, the inventive methodologies applying PC supported systems for portioning retinal vessels winding up increasingly significant and coming nearer. Various kinds of retinal vessels segmentation strategies discussed by using Deep Learning methods. At that point, the pre-processing activities and the best in class strategies for retinal vessels distinguishing proof are presented.

scholarly journals Reliability of Manual Vascular Segmentation for Retinal Fractal Dimension using Peek Retinatm

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1560-1564
Keyword(s):  
Fractal Dimension ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Image ◽  
Retinal Images ◽  
Retinal Vasculature ◽  
Manual Segmentation ◽  
Good Reliability ◽  
Fundus Camera ◽  
Df Analysis

Fractal dimension (Df) has been identified as indirect measure in quantifying the complexity of retinal vessel network which is useful for early detection of vascular changes. Reliability studies of Df measurement on retinal vasculature, has been conducted on retinal images processed by using semi-automated software which only permits image with 45ᵒ field of view (FOV). Smartphone-assisted fundus camera retinal image has a maximum 30ᵒ FOV which warrant manual processing in measuring the Df. Retinal blood vessels need to be manually segmented to produce binary images for retinal vasculatures Df measurement. Therefore, this study was conducted to determine the intragrader and intergrader reliability of manual segmentation of the retinal vasculature Df measurement from retinal images taken using a smartphone-assisted fundus camera Forty-five retinal images were captured using the Portable Eye Examination Kit Retina (Peek Retina™, Peek Vision Ltd, UK). Suitable image for Df analysis were selected based on gradable retinal image criteria which included; i) good image focus, ii) centered position of optic nerve head (ONH) and iii) significant blood vessel visibility. The images were cropped 0.5 disc diameters away from disc margin and resized to 500x500 pixels using GNU Image Manipulation Program Version 2.8.18 (GIMP, The GIMP Team, United States). Retinal vessels were manually traced by using layering capabilities for blood vessel segmentation. Df values of segmented blood vessels were measured by using Image J (National Institutes of Health, USA) and its plugin software, FracLac Version 2.5. Intragrader and intergrader reliability was determined by comparing the Df values between; two readings measured one week apart by a grader and readings from two different graders, respectively, using intraclass correlation coefficient (ICC) and Bland-Altman graphical plots. Intragrader agreement for retinal Df showed good reliability with ICC of 0.899 (95% CI: 0.814–0.945). Bland Altman analysis indicated good agreement between Df values at different grading time (mean difference 0.0050; 95% CI:-0.0001–0.0101). Intergrader reliability for retinal Df was high with ICC of 0.814 (95% CI: 0.459–0.919). Bland Altman plot revealed good intergrader agreement for retinal Df between two graders with a bias value of 0.0158 (95% CI: 0.0092–0.0223). In conclusion, manual segmentation of retinal image captured by smartphone-assisted fundus camera has good reliability (0.75 < ICC < 0.9) for Df analysis to study the morphology of retinal vasculatures.

Development of an automated method for detecting abnormal blood vessels on retinal images

2004 ◽  
Vol 1268 ◽  
pp. 1363
Author(s):  
Yuji Hatanaka ◽  
Yusuke Sakakibara ◽  
Takeshi Hara ◽  
Hiroshi Fujita ◽  
Akira Aoyama ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Retinal Images ◽  
Automated Method

scholarly journals Retinal vessel feature extraction from fundus image using image processing techniques

2018 ◽  
Vol 7 (2) ◽  
pp. 687
Author(s):  
R. Lavanya ◽  
G. K. Rajini ◽  
G. Vidhya Sagar
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Vessel Detection ◽  
Proper Diagnosis ◽  
Processing Techniques

Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood vessels in the retina become weakened and leak, forming small hemorrhages. As the disease progress, blood vessels may block, and sometimes leads to permanent vision loss. To help Clinicians in diagnosis of diabetic retinopathy in retinal images with an early detection of abnormalities with automated tools.Methods: Fundus photography is an imaging technology used to capture retinal images in diabetic patient through fundus camera. Adaptive Thresholding is used as pre-processing techniques to increase the contrast, and filters are applied to enhance the image quality. Morphological processing is used to detect the shape of blood vessels as they are nonlinear in nature.Results: Image features like, Mean and Standard deviation and entropy, for textural analysis of image with Gray Level Co-occurrence Matrix features like contrast and Energy are calculated for detected vessels.Conclusion: In diabetic patients eyes are affected severely compared to other organs. Early detection of vessel structure in retinal images with computer assisted tools may assist Clinicians for proper diagnosis and pathology. 

scholarly journals Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

2020 ◽  
Vol 44 (10) ◽  
Author(s):  
Debasis Maji ◽  
Arif Ahmed Sekh
Keyword(s):  
Blood Vessels ◽  
Machine Learning Algorithms ◽  
Retinal Images ◽  
Fundus Images ◽  
Hard Exudates ◽  
Automated Method ◽  
Eye Problems ◽  
Automated Grading ◽  
Automatic Grading ◽  
Automated Screening

Abstract Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

scholarly journals SEGMENTING THE EYE FUNDUS IMAGES FOR IDENTIFICATION OF BLOOD VESSELS

2012 ◽  
Vol 17 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Gediminas Balkys ◽  
Gintautas Dzemyda
Keyword(s):  
Blood Vessels ◽  
Binary Classification ◽  
Threshold Value ◽  
Retinal Images ◽  
Fundus Images ◽  
Fundus Image ◽  
Image Characteristics ◽  
Selection For ◽  
Binarization Method ◽  
Local Image

Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.

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