scholarly journals A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2017
Author(s):  
Sonali Dash ◽  
Sahil Verma ◽  
Kavita Kavita ◽  
Md. Sameeruddin Khan ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Hybrid Method ◽  
Noisy Data ◽  
Curvelet Transform ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Simulation Results ◽  
Ophthalmic Disease

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

scholarly journals Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

2022 ◽  
Vol 71 (2) ◽  
pp. 2459-2476
Author(s):  
Sonali Dash ◽  
Sahil Verma ◽  
Kavita ◽  
N. Z. Jhanjhi ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Curvelet Transform ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Edge Preserving ◽  
Blood Vessel Segmentation

scholarly journals Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

2021 ◽  
Author(s):  
Sanjeewani NA ◽  
arun kumar yadav ◽  
Mohd Akbar ◽  
mohit kumar ◽  
Divakar Yadav
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Vessel Segmentation ◽  
Great Success ◽  
Retinal Blood Vessel ◽  
Data Set ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

A New Deeply Convolutional Neural Network Architecture for Retinal Blood Vessel Segmentation

2020 ◽  
pp. 2157001
Author(s):  
Mali Mohammedhasan ◽  
Harun Uğuz
Keyword(s):  
Neural Network ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Blood Vessels ◽  
Network Architecture ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Low Level ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

This paper proposes an incoming Deep Convolutional Neural Network (CNN) architecture for segmenting retinal blood vessels automatically from fundus images. Automatic segmentation performs a substantial role in computer-aided diagnosis of retinal diseases; it is of considerable significance as eye diseases as well as some other systemic diseases give rise to perceivable pathologic changes. Retinal blood vessel segmentation is challenging because of the excessive changes in the morphology of the vessels on a noisy background. Previous deep learning-based supervised methods suffer from the insufficient use of low-level features which is advantageous in semantic segmentation tasks. The proposed architecture makes use of both high-level features and low-level features to segment retinal blood vessels. The major contribution of the proposed architecture concentrates on two important factors; the first in its supplying of extremely modularized network architecture of aggregated residual connections which enable us to copy the learned layers from the shallower model and developing additional layers to identity mapping. The second is to improve the utilization of computing resources within the network. This is achieved through a skillfully crafted design that allows for increased depth and width of the network while maintaining the stability of its computational budget. Experimental results show the effectiveness of using aggregated residual connections in segmenting retinal vessels more accurately and clearly. Compared to the best existing methods, the proposed method outperformed other existing methods in different measures, comprised less false positives at fine vessels, and caressed more clear lines with sufficient details like the human annotator.

scholarly journals An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jianqing Gao ◽  
Guannan Chen ◽  
Wenru Lin
Keyword(s):  
Random Walk ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Morphological Characteristics ◽  
Maximum Intensity Projection ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Structured Analysis

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

scholarly journals Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yuliang Ma ◽  
Zhenbin Zhu ◽  
Zhekang Dong ◽  
Tao Shen ◽  
Mingxu Sun ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Current Problem ◽  
Matched Filter ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Vessel Enhancement ◽  
Better Than

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

scholarly journals Retinal blood vessel segmentation based on heuristic image analysis

2019 ◽  
Vol 16 (1) ◽  
pp. 227-245 ◽  
Author(s):  
Maja Braovic ◽  
Darko Stipanicev ◽  
Ljiljana Seric
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Vessel Segmentation ◽  
Optic Disk ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

scholarly journals Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

2021 ◽  
Author(s):  
Sanjeewani NA ◽  
arun kumar yadav ◽  
Mohd Akbar ◽  
mohit kumar ◽  
Divakar Yadav
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Vessel Segmentation ◽  
Great Success ◽  
Retinal Blood Vessel ◽  
Data Set ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

Sign in / Sign up

Export Citation Format

Share Document