scholarly journals Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

2014 ◽  
Vol 53 ◽  
Author(s):  
Mandlenkosi Victor Gwetu ◽  
Jules Raymond Tapamo ◽  
Serestina Viriri
Keyword(s):  
Blood Vessels ◽  
Retinal Image ◽  
Research Work ◽  
Data Sets ◽  
Background Illumination ◽  
Illumination Variation ◽  
Automatic Thresholding ◽  
Retinal Blood Vessels ◽  
Zero Integral ◽  
Accuracy Rates

Although computerized retinal image blood vessel segmentation has been extensively researched, there is still room for improvement in the quality of the segmented images. Since retinal image analysis is still widely used in the diagnosis of diabetic retinopathy, efficient and accurate image characterization techniques are required. Previous work has mainly focused on improving segmentation accuracy rates with little regard to the false positives that are produced by illumination variation. This research work presents a hybrid approach towards the segmentation of retinal blood vessels. New approaches towards the reduction of background illumination variation are proposed using normalized Gabor filtering. These are the base-offset encoding and a modified version of an existing zero-integral kernel technique. The valley emphasis automatic thresholding scheme is used to segment the Gabor response images. Experiments are conducted on the DRIVE and STARE retinal image data sets. Accuracy rates of up to 94% are achieved through the zero-integral and base offset methods. This is comparable with results from literature, where the same data sets are segmented using other classification techniques. The median-offset method is found to most effectively reduce background illumination variation.

scholarly journals A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mehmet Bahadır Çetinkaya ◽  
Hakan Duran
Keyword(s):  
Blood Vessels ◽  
Retinal Image ◽  
Heuristic Algorithms ◽  
Mean Squared Error ◽  
Harmony Search ◽  
Retinal Vessel ◽  
Convergence Speed ◽  
Low Contrast ◽  
Squared Error ◽  
Retinal Blood Vessels

AbstractComputer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

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.

scholarly journals Retinal Image Matching Using Hierarchical Vascular Features

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Alauddin Bhuiyan ◽  
Ecosse Lamoureux ◽  
Baikunth Nath ◽  
Kotagiri Ramamohanarao ◽  
Tien Y. Wong
Keyword(s):  
Longitudinal Study ◽  
Blood Vessels ◽  
Retinal Image ◽  
Image Matching ◽  
Feature Vector ◽  
Person Identification ◽  
Feature Vectors ◽  
Invariant Features ◽  
Retinal Blood Vessels ◽  
Initial Results

We propose a method for retinal image matching that can be used in image matching for person identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image, and a feature vector is constructed for each of the vessel segments in the retinal blood vessels. The feature vectors are represented in a tree structure with maintaining the vessel segments actual hierarchical positions. Using these feature vectors, corresponding images are matched. The method identifies the same vessel in the corresponding images for comparing the desired feature(s). Initial results are encouraging and demonstrate that the proposed method is suitable for image matching and patient longitudinal study.

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.

Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and Sauvola Thresholding

2021 ◽  
Author(s):  
Erwin ◽  
Hadrians Kesuma Putra ◽  
Bambang Suprihatin ◽  
Fathoni
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Image ◽  
Histogram Equalization ◽  
Retinal Blood Vessel ◽  
Retinal Blood Vessels ◽  
Convolution Filter ◽  
Image Improvement ◽  
Vessel Extraction ◽  
Convolution Filters

The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37% for DRIVE with a runtime of 1.77[Formula: see text]s and 95.17% for STARE with 2.05[Formula: see text]s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant.

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.

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 Investigation of Bilateral Similarity in Central Retinal Blood Vessels

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sangeeta Biswas ◽  
Johan Rohdin ◽  
Andrii Kavetskyi ◽  
Gabriel Saraiva ◽  
Angkan Biswas ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Retinal Blood Vessels

The accuracy of pedicle screw placement using intraoperative image guidance systems

2014 ◽  
Vol 20 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Alexander Mason ◽  
Renee Paulsen ◽  
Jason M. Babuska ◽  
Sharad Rajpal ◽  
Sigita Burneikiene ◽  
...  
Keyword(s):  
Pedicle Screw ◽  
Image Guidance ◽  
Pedicle Screws ◽  
Screw Placement ◽  
Data Sets ◽  
Pedicle Screw Placement ◽  
Fluoroscopic Image ◽  
Fluoroscopic Navigation ◽  
Guidance Systems ◽  
Accuracy Rates

Object Several retrospective studies have demonstrated higher accuracy rates and increased safety for navigated pedicle screw placement than for free-hand techniques; however, the accuracy differences between navigation systems has not been extensively studied. In some instances, 3D fluoroscopic navigation methods have been reported to not be more accurate than 2D navigation methods for pedicle screw placement. The authors of this study endeavored to identify if 3D fluoroscopic navigation methods resulted in a higher placement accuracy of pedicle screws. Methods A systematic analysis was conducted to examine pedicle screw insertion accuracy based on the use of 2D, 3D, and conventional fluoroscopic image guidance systems. A PubMed and MEDLINE database search was conducted to review the published literature that focused on the accuracy of pedicle screw placement using intraoperative, real-time fluoroscopic image guidance in spine fusion surgeries. The pedicle screw accuracy rates were segregated according to spinal level because each spinal region has individual anatomical and morphological variations. Descriptive statistics were used to compare the pedicle screw insertion accuracy rate differences among the navigation methods. Results A total of 30 studies were included in the analysis. The data were abstracted and analyzed for the following groups: 12 data sets that used conventional fluoroscopy, 8 data sets that used 2D fluoroscopic navigation, and 20 data sets that used 3D fluoroscopic navigation. These studies included 1973 patients in whom 9310 pedicle screws were inserted. With conventional fluoroscopy, 2532 of 3719 screws were inserted accurately (68.1% accuracy); with 2D fluoroscopic navigation, 1031 of 1223 screws were inserted accurately (84.3% accuracy); and with 3D fluoroscopic navigation, 4170 of 4368 screws were inserted accurately (95.5% accuracy). The accuracy rates when 3D was compared with 2D fluoroscopic navigation were also consistently higher throughout all individual spinal levels. Conclusions Three-dimensional fluoroscopic image guidance systems demonstrated a significantly higher pedicle screw placement accuracy than conventional fluoroscopy or 2D fluoroscopic image guidance methods.

Insulin Receptors in Calf and Human Retinal Blood Vessels

1983 ◽  
Vol 15 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Edward Cotlier ◽  
Charles Davidson
Keyword(s):  
Blood Vessels ◽  
Insulin Receptors ◽  
Retinal Blood Vessels
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