DETECTION OF BLOOD VESSELS FROM RETINAL IMAGES USING WATERSHED TRANSFORMATION

2009 ◽  
Vol 09 (04) ◽  
pp. 633-642 ◽  
Author(s):  
A. BESSAID ◽  
A. FEROUI ◽  
M. MESSADI
Keyword(s):  
Blood Vessels ◽  
Automated Analysis ◽  
Retinal Images ◽  
Fundus Images ◽  
Vascular Tree ◽  
Important Indicator ◽  
Watershed Transformation ◽  
Low Contrast ◽  
Retinal Blood Vessels ◽  
Diagnostic Step

Automated analysis and interpretation of retinal images has become an incontournable diagnostic step in ophthalmology. Retinal blood vessels morphology can be an important indicator for diseases such as diabetic retinopathy; and their detection also serves for image registration. This paper presents a method based on mathematical morphology for extraction of vascular tree in color retinal image with low contrast. It consists in contrast enhancement and application of watershed transformation in order to segment blood vessels in digital fundus images.

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%.

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%.

scholarly journals PREMATURE INFANT BLOOD VESSEL SEGMENTATION OF RETINAL IMAGES BASED ON HYBRID METHOD FOR THE DETERMINATION OF TORTUOSITY

2020 ◽  
Vol 50 (2) ◽  
pp. 49-57
Author(s):  
Alice Krestanova ◽  
Jan Kubicek ◽  
Marek Penhaker ◽  
Juraj Timkovic
Keyword(s):  
Image Processing ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Images ◽  
Morphological Operations ◽  
Suggested Model ◽  
Low Contrast ◽  
Retinal Blood Vessels ◽  
Good Contrast

For the retinal blood vessels segmentation, we used a method, which is based on the morphological operations. The output of this process is extracted retinal binary image, where is situated main blood vessels. In this paper is used dataset of images (2800 images) from device RetCam3. Before applying the image processing, it was selected 30 images with diagnosed pre-plus diseases, and it is divided into two groups with low contrast and good contrast images. In the next part of the analysis, it was analyzing and showing blood vessels with tortuosity. Tortuosity is a symptom of ROP (retinopathy of prematurity). The clinical physicians evaluate tortuosity by visual comparison of the retinal images images. For this reason, it was suggested model which can automatically indicate the tortuosity of the retinal blood vessels by setting a threshold of the blood vessels curvature.

Optimal Feature Selection and Extraction for Eye Disease Diagnosis

2019 ◽  
pp. 100-112
Author(s):  
Alli P. ◽  
S. K. Somasundaram
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Blood Vessels ◽  
Disease Diagnosis ◽  
Eye Disease ◽  
Fundus Images ◽  
Low Contrast ◽  
Performance Factors ◽  
Time Sensitivity ◽  
Retinal Fundus

Ophthalmologists utilize retinal fundus images of humans for the detection, diagnosis, and prediction of many eye diseases. Automatic scrutiny of fundus images are foremost apprehension for ophthalmologists and investigators. The manual recognition of blood vessels is most deceptive because the blood vessels in a fundus image are multifaceted and with low contrast. Unearthing of blood vessels proffers information on pathological transformation and can smooth the progress of rating diseases severity or mechanically diagnosing the diseases. The manual recognition method turns out to be annoying. Consequently, the automatic recognition of blood vessels is also more significant. For extracting the vessel in fundus images unswerving and habitual methods are obligatory. The proposed methodology is designed to effectively diagnose the eye disease by performing feature extraction succeeded by feature selection and to improve the performance factors such as feature extraction ratio, feature selection time, sensitivity, and specificity when compared to the state-of-art methods.

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.

scholarly journals Segmentation of Retinal Blood Vessels Based on Cake Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xi-Rong Bao ◽  
Xin Ge ◽  
Li-Huang She ◽  
Shi Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
False Positive Rate ◽  
New Method ◽  
Real Component ◽  
Low Contrast ◽  
Small Vessels ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Positive Rate

Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate.

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.

Extraction of Blood Vessels in Retina

2018 ◽  
Vol 11 (4) ◽  
pp. 122-136
Author(s):  
Thamer Mitib Al Sariera ◽  
Lalitha Rangarajan
Keyword(s):  
Blood Vessels ◽  
Spatial Filter ◽  
Vascular Tree ◽  
Retinal Blood Vessels ◽  
Tracking Process ◽  
Low Pass ◽  
Novel Method ◽  
Local Window ◽  
Spatial Locations ◽  
Optimum Direction

This article presents a novel method to extract retinal vascular tree automatically. The proposed method consists of four steps; smoothing image using low pass spatial filter to reduce spurious noise in the image; extracting candidate borders of the vessels based on a local window property; tracking process, starting with a candidate pixel and following in the optimum direction with monitoring the connectivity of the vessel twin border; constructing the whole tree of retinal blood vessels by connecting the vessel segments based on their spatial locations, widths and directions. The algorithm was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images.

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