Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

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.

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/2070/1/012104 ◽

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.

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Efficient BFCN for Automatic Retinal Vessel Segmentation

Journal of Ophthalmology ◽

10.1155/2020/6439407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

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.

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Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach

Scientific Programming ◽

10.1155/2021/5520407 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Zihe Huang ◽

Ying Fang ◽

He Huang ◽

Xiaomei Xu ◽

Jiwei Wang ◽

...

Keyword(s):

Blood Vessels ◽

Structural Changes ◽

Area Under The Curve ◽

Image Data ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Circulation System ◽

Retinal Blood Vessels ◽

Image Patches ◽

Retinal Vessel Segmentation

Retinal blood vessels are the only deep microvessels in the blood circulation system that can be observed directly and noninvasively, providing us with a means of observing vascular pathologies. Cardiovascular and cerebrovascular diseases, such as glaucoma and diabetes, can cause structural changes in the retinal microvascular network. Therefore, the study of effective retinal vessel segmentation methods is of great significance for the early diagnosis of cardiovascular diseases and the vascular network’s quantitative results. This paper proposes an automatic retinal vessel segmentation method based on an improved U-Net network. Firstly, the image patches are rotated to amplify the image data, and then, the RGB fundus image is preprocessed by normalization. Secondly, after the improved U-Net model is constructed with 23 convolutional layers, 4 pooling layers, 4 upsampling layers, 2 dropout layers, and Squeeze and Excitation (SE) block, the extracted image patches are utilized for training the model. Finally, the fundus images are segmented through the trained model to achieve precise extraction of retinal blood vessels. According to experimental results, the accuracy of 0.9701, 0.9683, and 0.9698, sensitivity of 0.8011, 0.6329, and 0.7478, specificity of 0.9849, 0.9967, and 0.9895, F1-Score of 0.8099, 0.8049, and 0.8013, and area under the curve (AUC) of 0.8895, 0.8845, and 0.8686 were achieved on DRIVE, STARE, and HRF databases, respectively, which is better than most classical algorithms.

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Retinal Vessel Analysis for Detection of Glaucoma

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37199 ◽

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.

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HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation

PLoS ONE ◽

10.1371/journal.pone.0257013 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257013

Author(s):

Xiaolong Hu ◽

Liejun Wang ◽

Shuli Cheng ◽

Yongming Li

Keyword(s):

Deep Learning ◽

Blood Vessels ◽

Structural Information ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Convolutional Network ◽

Retinal Blood Vessels ◽

Retinal Vessel Segmentation ◽

Ophthalmic Diseases ◽

Sensitivity Specificity

The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to the early treatment and initiative prevention of ophthalmic diseases. In our work, we propose a hierarchical dilation convolutional network (HDC-Net) to extract retinal vessels in a pixel-to-pixel manner. It utilizes the hierarchical dilation convolution (HDC) module to capture the fragile retinal blood vessels usually neglected by other methods. An improved residual dual efficient channel attention (RDECA) module can infer more delicate channel information to reinforce the discriminative capability of the model. The structured Dropblock can help our HDC-Net model to solve the network overfitting effectively. From a holistic perspective, the segmentation results obtained by HDC-Net are superior to other deep learning methods on three acknowledged datasets (DRIVE, CHASE-DB1, STARE), the sensitivity, specificity, accuracy, f1-score and AUC score are {0.8252, 0.9829, 0.9692, 0.8239, 0.9871}, {0.8227, 0.9853, 0.9745, 0.8113, 0.9884}, and {0.8369, 0.9866, 0.9751, 0.8385, 0.9913}, respectively. It surpasses most other advanced retinal vessel segmentation models. Qualitative and quantitative analysis demonstrates that HDC-Net can fulfill the task of retinal vessel segmentation efficiently and accurately.

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Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8822407 ◽

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.

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Deep Att-ResGAN: A Retinal Vessel Segmentation Network for Color Fundus Images

Traitement du signal ◽

10.18280/ts.380505 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1309-1317

Author(s):

Jie Zhao ◽

Qianjin Feng

Keyword(s):

Retinal Vessel ◽

Vessel Segmentation ◽

Original Image ◽

Generative Adversarial Network ◽

Low Contrast ◽

Adversarial Network ◽

Vessel Structure ◽

Retinal Vessel Segmentation ◽

Vessel Extraction ◽

Public Datasets

Retinal vessel segmentation plays a significant role in the diagnosis and treatment of ophthalmological diseases. Recent studies have proved that deep learning can effectively segment the retinal vessel structure. However, the existing methods have difficulty in segmenting thin vessels, especially when the original image contains lesions. Based on generative adversarial network (GAN), this paper proposes a deep network with residual module and attention module (Deep Att-ResGAN). The network consists of four identical subnetworks. The output of each subnetwork is imported to the next subnetwork as contextual features that guide the segmentation. Firstly, the problems of the original image, namely, low contrast, uneven illumination, and data insufficiency, were solved through image enhancement and preprocessing. Next, an improved U-Net was adopted to serve as the generator, which stacks the residual and attention modules. These modules optimize the weight of the generator, and enhance the generalizability of the network. Further, the segmentation was refined iteratively by the discriminator, which contributes to the performance of vessel segmentation. Finally, comparative experiments were carried out on two public datasets: Digital Retinal Images for Vessel Extraction (DRIVE) and Structured Analysis of the Retina (STARE). The experimental results show that Deep Att-ResGAN outperformed the equivalent models like U-Net and GAN in most metrics. Our network achieved accuracy of 0.9565 and F1 of 0.829 on DRIVE, and accuracy of 0.9690 and F1 of 0.841 on STARE.

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MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction

Applied Sciences ◽

10.3390/app12010403 ◽

2021 ◽

Vol 12 (1) ◽

pp. 403

Author(s):

Lin Pan ◽

Zhen Zhang ◽

Shaohua Zheng ◽

Liqin Huang

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Automatic Segmentation ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Multitask Learning ◽

Learning Network ◽

Centerline Extraction ◽

Retinal Vessel Segmentation ◽

And Performance

Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.

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DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

PLoS ONE ◽

10.1371/journal.pone.0261698 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261698

Author(s):

Mohsin Raza ◽

Khuram Naveed ◽

Awais Akram ◽

Nema Salem ◽

Amir Afaq ◽

...

Keyword(s):

Deep Learning ◽

Blood Vessels ◽

Medical Image Analysis ◽

Retinal Disease ◽

Disease Diagnosis ◽

Vessel Segmentation ◽

Diabetic Patients ◽

Retinal Vasculature ◽

Retinal Blood Vessels ◽

Dense Aggregation

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

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

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2020-0089 ◽

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.

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