scholarly journals Mathematical Morphology based Retinal Image Blood Vessels Segmentation

2019 ◽  
Vol 8 (12) ◽  
pp. 2914-2920
Keyword(s):  
Blood Vessels ◽  
Mathematical Morphology ◽  
Retinal Image ◽  
Retinal Vessel ◽  
Morphological Processing ◽  
Evaluation Metrics ◽  
Health Study ◽  
Retinal Images ◽  
Suggested Approach ◽  
Sensitivity Specificity

It is necessary to verify the state of blood vessel network in the retina for diagnosing various issues associated with eyes. In this research paper, an involuntary retinal vessel segmentation using mathematical morphology is proposed. The contrast of the retinal images is enhanced by contrast limited adaptive histogram equalisation technique. Ten blood vessels of the enhanced retinal image are detected using morphological processing. The hysteresis thresholding is applied on the blood vessels detected image to remove the unwanted back ground detail. Finally the properly segmented binary image of the retinal vessel is obtained using post processing process. Results of the presented method are verified by using most widely used for benchmarking retinal image databases such as, Child Heart and Health Study in England (CHASE_DB1) and Digital Retinal Images for Vessel Extraction (DRIVE) database by computing the evaluation metrics such as sensitivity, specificity, accuracy and precision. The better evaluation metrics achieved for the DRIVE dataset are 0.7493, 0.9687, 0.9524 and 0.6590, and the worst values are 0.6621, 0.9411, 0.9137 and 0.5491. The best evaluation metrics values for the CHASE_DB1 dataset are 0.5058, 0.8947, 0.9382 and 0.8856, and the worst values are 0.5639, 0.9581, 0.9137 and 0.7110. The investigational results show that the suggested approach provides the excellent accuracy in comparison with other approaches

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.

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 Efficient Segmentation and Registration of Retinal Image Using Gumble Probability Distribution and BRISK Feature

2020 ◽  
Vol 37 (5) ◽  
pp. 855-864
Author(s):  
Nagendra Pratap Singh ◽  
Vibhav Prakash Singh
Keyword(s):  
Retinal Image ◽  
Matched Filter ◽  
Computation Time ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Feature Descriptor ◽  
Retinal Diseases ◽  
Vessel Structure ◽  
Feature Based ◽  
Image Pairs

The registration of segmented retinal images is mainly used for the diagnosis of various diseases such as glaucoma, diabetes, and hypertension, etc. These retinal diseases depend on the retinal vessel structure. The fast and accurate registration of segmented retinal images helps to identify the changes in vessels and the diagnosis of the diseases. This paper presents a novel binary robust invariant scalable key point (BRISK) feature-based segmented retinal image registration approach. The BRISK framework is an efficient keypoint detection, description, and matching approach. The proposed approach contains three steps, namely, pre-processing, segmentation using matched filter based Gumbel pdf, and BRISK framework for registration of segmented source and target retinal images. The effectiveness of the proposed approach is demonstrated by evaluating the normalized cross-correlation of image pairs. Based on the experimental analysis, it has been observed that the performance of the proposed approach is better in both aspect, registration performance as well as computation time with respect to SURF and Harris partial intensity invariant feature descriptor based registration.

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.

A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation

2017 ◽  
pp. 2063-2081
Author(s):  
Ahmed Hamza Asad ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien
Keyword(s):  
Blood Vessels ◽  
Automatic Segmentation ◽  
Retinal Vessel ◽  
Ant Colony ◽  
Significant Feature ◽  
Retinal Images ◽  
Ant Colony System ◽  
Heuristic Function ◽  
Retinal Vessel Segmentation ◽  
Baseline Approach

The automatic segmentation of blood vessels in retinal images is the crucial stage in any retina diagnosis systems. This article discussed the impact of two improvements to the previous baseline approach for automatic segmentation of retinal blood vessels based on the ant colony system. The first improvement is in features where the length of previous features vector used in segmentation is reduced to the half since four less significant features are replaced by a new more significant feature when applying the correlation-based feature selection heuristic. The second improvement is in ant colony system where a new probability-based heuristic function is applied instead of the previous Euclidean distance based heuristic function. Experimental results showed the improved approach gives better performance than baseline approach when it is tested on DRIVE database of retinal images. Also, the statistical analysis demonstrated that was no statistically significant difference between the baseline and improved approaches in the sensitivity (0.7388± 0.0511 vs. 0.7501±0.0385, respectively; P = 0.4335). On the other hand, statistically significant improvements were found between the baseline and improved approaches for specificity and accuracy (P = 0.0024 and 0.0053, respectively). It was noted that the improved approach showed an increase of 1.1% in the accuracy after applying the new probability-based heuristic function.

Retinal Blood Vessel Extraction From Fundus Images Using Improved Otsu Method

2019 ◽  
Vol 10 (2) ◽  
pp. 21-43
Author(s):  
Jyotiprava Dash ◽  
Nilamani Bhoi
Keyword(s):  
Blood Vessels ◽  
Histogram Equalization ◽  
Optic Disk ◽  
Health Study ◽  
Retinal Images ◽  
Second Phase ◽  
Fast Time ◽  
Basic Task ◽  
Otsu Method ◽  
Vessel Extraction

In the present time, the identification of blood vessels is a basic task for diagnosis of various eye abnormalities. So, this article offers an instinctive approach for identification of blood vessels in ophthalmoscope images. This approach includes three different phases: pre-processing, vessel extraction and post-processing for getting a final vessel segmentation outcome. In the presented method, formerly log transformation and contrast limited adaptive histogram equalization are used for the enhancement of retinal images. The enhanced image is then filtered using a morphological opening operation and subsequently the optic disk is removed. The second phase includes the application of the improved Otsu method on the pre-processed image for the identification of blood vessels. Lastly, the resultant vessel-segmented image is obtained by using the morphological cleaning operation. The proposed method is fast, time efficient, and gives consistent accuracy for all retinal images. It is more robust and easier to implement compared to other traditional methods. The performance of the presented method is evaluated using ten different mathematical measures. It achieves average sensitivity, specificity and accuracy of 0.710, 0.982 and 0.956 for the digital retinal images for vessel extraction (DRIVE) database, 0.738, 0.982 and 0.954 for the structure analysis of the retina (STARE) database and 0.737, 0.964 and 0.949 for the child heart and health study in England (CHASE_DB1) database. The presented method also performs better in segmenting thin vessels by giving average accuracies of 0.964, 0.954 and 0.965 for DRIVE, STARE and CHASE_DB1 databases respectively.

scholarly journals A Multi-Scale Residual Attention Network for Retinal Vessel Segmentation

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 24
Author(s):  
Yun Jiang ◽  
Huixia Yao ◽  
Chao Wu ◽  
Wenhuan Liu
Keyword(s):  
Blood Vessels ◽  
Receptive Fields ◽  
Retinal Vessel ◽  
Negative Influence ◽  
Deep Convolutional Neural Networks ◽  
Attention Network ◽  
Multi Scale ◽  
The Difference ◽  
Sensitivity Specificity ◽  
F Measure

Accurate segmentation of retinal blood vessels is a key step in the diagnosis of fundus diseases, among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness. Most segmentation methods based on deep convolutional neural networks can effectively extract features. However, convolution and pooling operations also filter out some useful information, and the final segmented retinal vessels have problems such as low classification accuracy. In this paper, we propose a multi-scale residual attention network called MRA-UNet. Multi-scale inputs enable the network to learn features at different scales, which increases the robustness of the network. In the encoding phase, we reduce the negative influence of the background and eliminate noise by using the residual attention module. We use the bottom reconstruction module to aggregate the feature information under different receptive fields, so that the model can extract the information of different thicknesses of blood vessels. Finally, the spatial activation module is used to process the up-sampled image to further increase the difference between blood vessels and background, which promotes the recovery of small blood vessels at the edges. Our method was verified on the DRIVE, CHASE, and STARE datasets. Respectively, the segmentation accuracy rates reached 96.98%, 97.58%, and 97.63%; the specificity reached 98.28%, 98.54%, and 98.73%; and the F-measure scores reached 82.93%, 81.27%, and 84.22%. We compared the experimental results with some state-of-art methods, such as U-Net, R2U-Net, and AG-UNet in terms of accuracy, sensitivity, specificity, F-measure, and AUCROC. Particularly, MRA-UNet outperformed U-Net by 1.51%, 3.44%, and 0.49% on DRIVE, CHASE, and STARE datasets, respectively.

scholarly journals Retinal Vessel Segmentation Using an Entropy-Based Optimization Algorithm

2020 ◽  
Vol 15 (2) ◽  
pp. 61-79
Author(s):  
Sukhpreet Kaur ◽  
Kulwinder Singh Mann
Keyword(s):  
Blood Vessels ◽  
Optimization Algorithm ◽  
False Positive Rate ◽  
Retinal Vessel ◽  
Support Vector ◽  
Positive Rate ◽  
Long Time ◽  
Retinal Vessel Segmentation ◽  
Lion Optimization Algorithm ◽  
Sensitivity Specificity

This article presents an algorithm for the segmentation of retinal blood vessels for the detection of diabetic retinopathy eye diseases. This disease occurs in patients with untreated diabetes for a long time. Since this disease is related to the retina, it can eventually lead to vision impairment. The proposed algorithm is a supervised learning method of blood vessels segmentation in which the classification system is trained with the features that are extracted from the images. The proposed system is implemented on the images of DRIVE, STARE and CHASE_DB1 databases. The segmentation is done by forming clusters with the features of patterns. The features were extracted using independent component analysis and the classification is performed by support vector machines (SVM). The results of the parameters are grouped by accuracy, sensitivity, specificity, positive predictive value, false positive rate and are compared with particle swarm optimization (PSO), the firefly optimization algorithm (FA) and the lion optimization algorithm (LOA).

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