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 Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2297
Author(s):  
Toufique A. Soomro ◽  
Ahmed Ali ◽  
Nisar Ahmed Jandan ◽  
Ahmed J. Afifi ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Retinal Disease ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Low Contrast ◽  
The Public ◽  
Retinal Blood Vessels ◽  
Retinal Vessel Segmentation ◽  
Automated Screening ◽  
Traditional Image

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

scholarly journals Dilated Deep Neural Architectures for Improving Retinal Vessel Extraction

2021 ◽  
Author(s):  
Sathananthavathi V ◽  
Indumathi G
Keyword(s):  
Neural Network ◽  
Blood Vessels ◽  
Network Performance ◽  
Retinal Vessel ◽  
Large Field ◽  
Feature Maps ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Retinal Blood Vessels ◽  
Vessel Extraction

Abstract Human eye is an absolute sensory organ for vision. Eye sight is entirely accomplished by the blood flow in retinal vessels in eye. Diseases such as diabetes retinopathy, hypertension and arteriosclerosis cause change in branching pattern and diameter of retinal blood vessels leading to blindness. These changes can be analyzed by segmenting retinal blood vessel. Hence the retinal vasculature is recognized as the promising anatomical region for the diagnosis of several commonly seen diseases including cardiovascular related and diabetes. In this paper we propose two novel deep neural architectures named as Dilated fully convolved convolutional neural network (FCNN) and dilated depth concatenated neural network (DCNN) to segment the retinal blood vessels. The feature maps of fundus images are extracted by multiple dilated convolutional layers and due to the large field of view by dilation, pixel classification gets improved. The proposed work is evaluated for both the proposed architectures with and without dilation. It is observed from the obtained results that dilation enhances the network performance. To eliminate the non-uniform illumination and low contrast differences effect the preprocessed images are used for training the architectures. The proposed methodologies are experimented on the two publicly available databases DRIVE and STARE database.

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

scholarly journals Image processing-based model for tortuosity measurement of retinal blood vessels

2019 ◽  
Vol 2 (3) ◽  
pp. 43-67
Author(s):  
Sanyukta Chetia ◽  
SR Nirmala
Keyword(s):  
Image Processing ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Hypertensive Retinopathy ◽  
Retinal Diseases ◽  
Retinal Blood Vessel ◽  
Vessel Segment ◽  
Retinal Blood Vessels ◽  
Vessel Tortuosity

Purpose: The study of retinal blood vessel morphology is of great importance in retinal image analysis. The retinal blood vessels have a number of distinct features such as width, diameter, tortuosity, etc. In this paper, a method is proposed to measure the tortuosity of retinal blood vessels obtained from retinal fundus images. Tortuosity is a situation in which blood vessels become tortuous, that is, curved or non-smooth. It is one of the earliest changes that occur in blood vessels in some retinal diseases. Hence, its detection at an early stage can prevent the progression of retinal diseases such as diabetic retinopathy, hypertensive retinopathy, retinopathy of prematurity, etc. The present study focuses on the measurement of retinal blood vessel tortuosity for the analysis of hypertensive retinopathy. Hypertensive retinopathy is a condition in which the retinal vessels undergo changes and become tortuous due to long term high blood pressure. Early recognition of hypertensive retinopathy signs remains an important step in determining the target-organ damage and risk assessment of hypertensive patients. Hence, this paper attempts to estimate tortuosity using image-processing techniques that have been tested on artery and vein segments of retinal images. Design: Image processing-based model designed to measure blood vessel tortuosity. Methods: In this paper, a novel image processing-based model is proposed for tortuosity measurement. This parameter will be helpful for analyzing hypertensive retinopathy. To test the eff ectiveness of the system in determining tortuosity, the method is first applied on a set of synthetically generated blood vessels. Then, the method is repeated on blood vessel (both artery and vein) segments extracted from retinal images collected from publicly available databases and on images collected from a local eye hospital. The blood vessel segment images that are used in the method are binary images where blood vessels are represented by white pixels (foreground), while black pixels represent the background. Vessels are then classified into normal, moderately tortuous, and severely tortuous by following the analysis performed on the images in the Retinal Vessel Tortuosity Data Set (RET-TORT) obtained from BioIm Lab, Laboratory of Biomedical Imaging (Padova, Italy). This database consists of 30 artery segments and 30 vein segments, which were manually ordered on the basis of increasing tortuosity by Dr. S. Piermarocchi, a retinal specialist belonging to the Department of Ophthalmology of the University of Padova (Italy). The artery and vein segments with the fewest number of turns are given a low tortuosity ranking, while those with the greatest number of turns are given a high tortuosity ranking by the expert. Based on this concept, our proposed method defines retinal image segments as normal when they present the fewest number of twists/turns, moderately tortuous when they present more twists/turns than normal but fewer than severely tortuous vessels, and severely tortuous when they present a greater number of twists/turns than moderately tortuous vessels. On implementing our image processing-based method on binary blood vessel segment images that are represented by white pixels, it is found that the vessel pixel (white pixels) count increases with increasing vessel tortuosity. That is, for normal blood vessels, the white pixel count is less compared to moderately tortuous and severely tortuous vessels. It should be noted that the images obtained from the different databases and from the local hospital for this experiment are not hypertensive retinopathy images. Instead, they are mostly normal eye images and very few of them show tortuous blood vessels. Results: The results of the synthetically generated vessel segment images from the baseline for the evaluation of retinal blood vessel tortuosity. The proposed method is then applied on the retinal vessel segments that are obtained from the DRIVE and HRF databases, respectively. Finally, to evaluate the capability of the proposed method in determining the tortuosity level of the blood vessels, the method is tested with a standard tortuous database, namely, the RET-TORT database. The results are then compared with the tortuosity level mentioned in the database. It was found that our method is able to classify blood vessel images as normal, moderately tortuous, and severely tortuous, with results closely matching the clinical ordering provided by the expert in the RET-TORT database. Subjective evaluation was also performed by research scholars and postgraduate students to cross-validate the results. Conclusion: The close correlation between the tortuosity evaluation using the proposed method and the clinical ordering of retinal vessels as provided by the retinal specialist in the RET-TORT database shows that, although simple, this method can evaluate the tortuosity of vessel segments effectively.  

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 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 Hybrid UNet Architecture based on Residual Learning of Fundus Images for Retinal Vessel Segmentation

2021 ◽  
Vol 2070 (1) ◽  
pp. 012104
Author(s):  
Sushma Nagdeote ◽  
Sapna Prabhu
Keyword(s):  
Blood Vessels ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Intensity Difference ◽  
Fundus Images ◽  
The Past ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Image ◽  
Retinal Vessel Segmentation ◽  
Retinal Fundus

Abstract This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.

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.

scholarly journals Efficient BFCN for Automatic Retinal Vessel Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yun Jiang ◽  
Falin Wang ◽  
Jing Gao ◽  
Wenhuan Liu
Keyword(s):  
Blood Vessels ◽  
Spatial Information ◽  
Contextual Information ◽  
Receptive Fields ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Low Contrast ◽  
Retinal Vessel Segmentation ◽  
Segmentation Image

Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, 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.

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