Automatic Segmentation Of Retinal Blood Vessels Research Articles

Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey

AbstractDiabetes can cause damage to the retina's blood vessels in the eye leading to diabetic retinopathy (DR). The images captured using a fundus camera are used to segment and study the blood vessel damage. Once the retina is damaged, it cannot be repaired. Therefore, early detection of blood vessel damage is the only way to control the progression of the disease. It allows physicians to provide timely and appropriate treatment to patients. The ophthalmologist can manually recognize and mark these vessels based on some clinical and geometrical features; however, it is time‐consuming. Extraction and segmentation play a significant role in showing the difference between healthy and newly developed abnormal vessels. Due to the increased diabetes population, automated systems have been designed to detect retinal blood vessels and assist ophthalmologists. Identifying, extracting, and examining blood vessels are intricate processes, specifically identifying new vessels in the retina. In medical image analysis, artificial intelligence and deep learning techniques have become widely used practices for automatic retinal blood vessel segmentation. We reviewed articles from 1989 to 2023, including handcrafted segmentation to recent deep‐learning techniques with available public datasets. We have concluded this article with an overview of observed parameters, calculations, and future directions for the analysis of retinal images. We believe this review article will help researchers identify research gaps in the field of DR.

Retinal blood vessel segmentation by using the MS-LSDNet network and geometric skeleton reconnection method

Automatic retinal blood vessel segmentation is a key link in the diagnosis of ophthalmic diseases. Recent deep learning methods have achieved high accuracy in vessel segmentation but still face challenges in maintaining vascular structural connectivity. Therefore, this paper proposes a novel retinal blood vessel segmentation strategy that includes three stages: vessel structure detection, vessel branch extraction and broken vessel segment reconnection. First, we propose a multiscale linear structure detection network (MS-LSDNet), which improves the detection ability of fine blood vessels by learning the types of rich hierarchical features. In addition, to maintain the connectivity of the vascular structure in the process of binarization of the vascular probability map, an adaptive hysteresis threshold method for vascular extraction is proposed. Finally, we propose a vascular tree structure reconstruction algorithm based on a geometric skeleton to connect the broken vessel segments. Experimental results on three publicly available datasets show that compared with current state-of-the-art algorithms, our strategy effectively maintains the connectivity of retinal vascular tree structure.

RETRACTED ARTICLE: Robust retinal blood vessel segmentation using convolutional neural network and support vector machine

In recent decades, automatic retinal blood vessel segmentation and classification (RBVSC) helps to determine many diseases such as glaucoma, hypertension, macular-degeneration, diabetes-mellitus, etc. The early recognition of these disorders is essential for preventing patients from blindness. In this work, a new supervised system was developed to enhance the performance of RBVSC. At first, the input retinal images were collected from two datasets such as: Digital Retinal Image for Vessel Extraction (DRIVE) and STARE (STructured Analysis of the Retina). Then, the retinal vessels were segmented utilizing mean orientation based super-pixel segmentation. Besides, Convolutional Neural Network (CNN) was applied to extract the feature vectors from segmented regions. Finally, a binary classifier [Support Vector Machine (SVM)] performs classification on the extracted features for classifying the “vessel” and “non-vessel” regions. The combination of CNN and SVM automatically learns the feature values from raw images and classifies the patterns easily. From the experimental study, the proposed system improved RBVSC up to 2–4% compared to other existing systems and classification methodologies: Deep Neural Network (DNN), Random Forest (RF) and Naive Bayes (NB) by means of specificity, accuracy, sensitivity and kappa index.

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests.