Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding

2017 ◽  
Vol 12 (1) ◽  
pp. 14-29 ◽  
Author(s):  
Abderrahmane Elbalaoui ◽  
Mohamed Fakir ◽  
Taifi khaddouj ◽  
Abdelkarim MERBOUHA
Keyword(s):  
Blood Vessels ◽  
Integration Method ◽  
Adaptive Thresholding ◽  
Second Step ◽  
Retinal Images ◽  
Ocular Diseases ◽  
Morphological Attributes ◽  
Retinal Blood Vessels ◽  
Vesselness Filter ◽  
Length Width

Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.

scholarly journals Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding

Ophthalmology ◽  
2018 ◽  
pp. 18-33
Author(s):  
Abderrahmane Elbalaoui ◽  
Mohamed Fakir ◽  
Taifi khaddouj ◽  
Abdelkarim MERBOUHA
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Adaptive Thresholding ◽  
Retinal Images ◽  
Ocular Diseases ◽  
Morphological Attributes ◽  
Retinal Blood Vessels ◽  
Vesselness Filter ◽  
Length Width

Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.

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 Retinal Image Quality Assessment: Portable Eye Examination Kit Retina (Peek Retina)Tm Versus 3d-Printed Ophthalmoscope (3dpo)

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1555-1559
Keyword(s):  
Image Quality ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Retinal Images ◽  
Retinal Blood Vessels ◽  
Df Analysis ◽  
3D Printed ◽  
Retinal Image Quality Assessment

Portable Eye Examination Kit retina (Peek Retina™, Peek Vision Ltd, UK) and 3D Printed Ophthalmoscope (3DPO) were identified to have acceptable image for retinal evaluation, however the retinal images quality in term of blood vessels visibility between both devices was uncertain. This study was conducted to compare the quality of image based on blood vessels visibility between Peek Retina and 3DPO for fractal dimension (Df) analysis. In this study, a total of 40 retinal images (nPEEK=20, n3DPO=20) of 20 participants were captured on random eyes. The best retinal images with good focus and significant retinal blood vessels visibility of Peek Retina and 3DPO were selected for image quality analysis. The retinal images were cropped approximately following the size of the cornea and resized to 900 by 900 pixels of resolution using GNU Image Manipulation Program Version 2.8.18 (GIMP). The images were randomly sorted as Retinal Image Quality Assessment (RIQA) generated by Google form. Likert scale was implemented to assess the preferences scale of retinal image quality in determining the visibility of retinal vasculature to be traced with four choices of response options (1; very difficult, 2; difficult, 3; easy and 4; very easy). Prior to the retinal image assessment, ten optometrists were asked to experience retinal blood vessels tracing and consequently evaluate the 40 images by choosing the scale options (1 to 4) based on visibility retinal blood vessels. Mann-Whitney test indicated that the blood vessel tracing was easier for Peek Retina (median = 3) than for 3DPO (median = 2), p < 0.0001. Retinal image captured by Peek Retina was rated as very easy (43.5%) for blood vessels tracing as compared to the image from 3DPO (17.0%)Error! Reference source not found.. Only 1.5% of the image captured by PEEK was considered as a very difficult for blood vessel tracing. Difficult and easy preference scales of blood vessel tracing for PEEK were 16.5% and 38.5% respectively. 34% of 3DPO retinal image was graded as difficult for blood vessel tracing followed by 28.5% (easy), 20.5% (very difficult) and 17.0% (very easy). The results indicate that a retinal image photographed by Peek Retina was more preferable in tracing retinal vascular network for Df analysis as compared to 3DPO.

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 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.

AN EFFICIENT APPROACH TO LOCATE OPTIC DISC CENTER, BLOOD VESSELS AND MACULA IN RETINAL IMAGES

2012 ◽  
Vol 24 (05) ◽  
pp. 425-434 ◽  
Author(s):  
D. Santhi ◽  
D. Manimegalai
Keyword(s):  
Blood Vessels ◽  
Optic Disc ◽  
Matched Filter ◽  
Geometric Approach ◽  
Directional Pattern ◽  
Main Step ◽  
Retinal Images ◽  
Disc Center ◽  
Retinal Blood Vessels ◽  
Segmentation Methods

Locating the Optic Disc (OD) and Macula Center (MC) is the main step in the automatic extraction of retinal anatomical structures for Diabetic Retinopathy (DR). In this work, a variety of vessel segmentation methods have been adopted for the identification of OD in retinal images. In order to locate OD, different segmentation algorithms like matched filter, spatially weighed Fuzzy C-means and threshold are used to detect the blood vessels from the retinal image. Matching the expected directional pattern of the retinal blood vessels is proposed to locate the OD center. The proposed vessel direction filter is resized and OD center is measured. Macula center (fovea) is located at a constant distance from optic disc. A new method with geometric approach is proposed for macula detection. The proposed methods have been evaluated using a STARE and DRIVE datasets which contains both normal and diseased retina. The proposed methods are successfully adopted for segmentation of blood vessels and location of OD and MC in both normal and abnormal images.

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.

scholarly journals Retinal Vessel Analysis for Detection of Glaucoma

2021 ◽  
Vol 9 (VII) ◽  
pp. 3661-3667
Author(s):  
Chandana R
Keyword(s):  
Intraocular Pressure ◽  
Optic Nerve ◽  
Blood Vessels ◽  
Vision Loss ◽  
Retinal Vessel ◽  
Automated Analysis ◽  
Vessel Segmentation ◽  
Morphological Attributes ◽  
Retinal Blood Vessels ◽  
Retinal Vessel Segmentation

Glaucoma, a disease of the optic nerve is caused by the increase in the intraocular pressure of the eye and results in damage to the optic nerve and vision loss. The main characteristic of glaucoma is an elevated intraocular pressure (IOP) and also the blood vessels get narrower. Vessel segmentation is one of the main steps in retinal automated analysis tools. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels are utilized for diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. Since, the numbers of blood vessels are more in the glaucomatous eye , glaucoma is detected by means of ISNT ratio. The image processing operations are performed on glaucomatous and normal eyes. We have chosen ten images of each from the database and ISNT ratio is calculated to get the area of blood vessels in each of the four quadrants of the eye and hence glaucoma is detected.

scholarly journals Comparative Analysis of Improved of FCM Algorithms in the Segmentation of Retinal Blood Vessels

2022 ◽  
Author(s):  
Imane Mehidi ◽  
Djamel Eddine Chouaib Belkhiat ◽  
Dalel Jabri
Keyword(s):  
Image Segmentation ◽  
Blood Vessels ◽  
Retinal Image ◽  
Color Image ◽  
Region Of Interest ◽  
Retinal Images ◽  
Fundus Image ◽  
Retinal Blood Vessels ◽  
Two Phases ◽  
Retinal Image Segmentation

Abstract The main purpose of identifying and locating the vessels of the retina is to specify the various tissues from the vascular structure of the retina (which could be differencied between wide or tight) of the background of the fundus image. There exist several segmentation techniques that are spreading to divide the retinal vessels, depending on the issues and complexity of the retinal images. Fuzzy c-means is one of the most often used algorithms for retinal image segmentation due to its effectiveness and speed. This paper analyzes the performance of improved FCM algorithms for retinal image segmentation in terms of their ability and capability in segmenting and isolating blood vessels. The process we followed in our paper consists of two phases. Firstly, the pre-processing phase, where the green channel is taken for the color image of the retina. Contrast enhancement is performed through CLAHE , proceeded by applying bottom-hat filtering to bottom-hat filtering is applied with the purpose to define the region of interest. Secondly, in the segmentation phase the obtained image is segmented using FCM algorithms. The algorithms chosen for this study are: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and, SSFCM performed on DRIVE and STARE databases. Experiments accomplished on DRIVE and STARE databases demonstrate that the DSFCM_N algorithm achieves better results on the DRIVE database, whereas the FGFCM algorithm provides better results on the STARE database in term of accuracy. Concerning time consumption. The FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images.

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