Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. Commonly used modalities in diagnosis of these diseases are fundus photography, scanning laser ophthalmoscope (SLO) and fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming and prone to human errors. In this research, we propose a new multi-modal framework for vessel segmentation called ELEMENT (v E sse L s E gmentation using M achine l E arning and co N nec T ivity). This framework consists of feature extraction and pixel-based classification using region growing and machine learning. The proposed features capture complementary evidence based on grey level and vessel connectivity properties. The latter information is seamlessly propagated through the pixels at the classification phase. ELEMENT reduces inconsistencies and speeds up the segmentation throughput. We analyze and compare the performance of the proposed approach against state-of-the-art vessel segmentation algorithms in three major groups of experiments, for each of the ocular modalities. Our method produced higher overall performance, with an overall accuracy of 97.40%, compared to 25 of the 26 state-of-the-art approaches, including six works based on deep learning, evaluated on the widely known DRIVE fundus image dataset. In the case of the STARE, CHASE-DB, VAMPIRE FA, IOSTAR SLO and RC-SLO datasets, the proposed framework outperformed all of the state-of-the-art methods with accuracies of 98.27%, 97.78%, 98.34%, 98.04% and 98.35%, respectively.

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector's direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.

https://www.mdpi.com/1424-8220/19/22/4949/pdf

A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation

Retinal blood vessels are the basis for clinical diagnosis of some diseases. Achieving automatic retinal vessel segmentation from fundus images is an important and challenging work. In this paper, a neural network architecture based on the Dense U-net using Inception module is proposed for retinal vessel segmentation. First, the skip connections in traditional U-net are replaced with Dense Block to achieve full fusion of features from shallow layers to deep layers. Then, the Inception module is applied to supersede the traditional convolution operation. Thus, vessel features corresponding to the convolution kernels of different sizes can be extracted. Finally, Generative Adversarial Networks (GAN) are adopted in the training phase. The Dense U-net using Inception module is treated as the generator of GAN, and a multilayer neural network is created as the discriminator of GAN. The generator and discriminator are trained alternately. The loss function is a combination of segmentation loss and GAN loss. So that segmentation results can be fitted to the ground truth from both pixel value and pixel distribution. The algorithm proposed in this paper is verified on the public Digital Retinal Images for Vessel Extraction (DRIVE) dataset, where the Dice rate reaches 82.15%, and the AU-ROC and AU-PR reach 0.9772 and 0.9058, respectively. Experiments show that the proposed algorithm is effective in realizing automatic retinal vessel segmentation.

/pdf/retinal-vessel-segmentation-combined-with-generative-3o8jkhi1qc.pdf

Retinal Vessel Segmentation Combined With Generative Adversarial Networks and Dense U-Net

Recent trends and advances in fundus image analysis: A review

Diabetic retinopathy is a retinal disease that affects diabetes patients and the major cause of blindness for age population. It is an asymptomatic disease, which involves changes to blood vessels that can cause them to bleed or leak fluid, causing distortion of vision. Therefore, the blood vessels extraction is very important to help ophthalmologists to recognise this disease at the first stage in order to prevent an eventual loss of vision. Consequently, in this paper, we propose an automatic system for diabetic retinopathy detection from color fundus images. The proposed approach is based on the segmentation of blood vessels and extracts the geometric features, which are used in the early detection of diabetic retinopathy. The Hessian matrix, ISODATA algorithm and active contour are used for the segmentation of the blood vessels, we have used. Finally, we have applied the decision tree CART algorithm to classify images into normal (NO-DR) or DR. The proposed system was tested on the DRIVE and Messidor databases and achieved an average sensitivity, specificity and accuracy of 89%, 99% and 96%, respectively for the segmentation of retinal vessels and 91%, 100% and 93%, respectively for the classification of diabetic retinopathy. Finally, the obtained results indicate that our approach is effective in diabetic retinopathy detection with better accuracy over existing methods, which can help ophthalmologists in early diagnosis.

Decision tree CART algorithm for diabetic retinopathy classification

In the present scenario, retinal image processing is toiling hard to get an efficient algorithm for de-noising and segmenting the blood vessel confined inside the closed curvature boundary. On this ground, this study presents a hybrid active contour model with a novel preprocessing technique to segment the retinal blood vessel in different fundus images. Contour driven black top-hat transformation and phase-based binarisation method have been implemented to preserve the edge and corner details of the vessels. In the proposed work, gradient vector flow (GVF)-based snake and balloon method are combined to achieve better accuracy over different existing active contour models. In the earlier active contour models, the snake cannot enter inside the closed curvature resulting loss of tiny blood vessels. To circumvent this problem, an inflation term F
 inf(balloon)
 with GVF-based snake is incorporated together to achieve the new internal energy of snake for effective vessel segmentation. The evaluation parameters are calculated over four publically available databases: STARE, DRIVE, CHASE, and VAMPIRE. The proposed model outperforms its competitors by calculating a wide range of proven parameters to prove its robustness. The proposed method achieves an accuracy of 0.97 for DRIVE & CHASE and 0.96 for STARE & VAMPIRE datasets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5413

Robust retinal blood vessel segmentation using hybrid active contour model

Robust segmentation of exudates from retinal surface using M-CapsNet via EM routing

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jnm.2477

Time‐frequency analysis and classification of power signals using adaptive cuckoo search algorithm

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.

/pdf/on-machine-learning-in-clinical-interpretation-of-retinal-13j90p89.pdf

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Diabetic Retinopathy (DR) is a diabetic mellitus complication that causes vision impairment and may lead to permanent blindness. The early signs of DR that appear on the retinal surface are microaneurysms, hemorrhages, hard exudates, and soft exudates. Hence the automatic detection of these retinal lesions assists in the early diagnosis of DR. This paper presents a novel deep learning model, MRT‐SegNet (Maximum Response Texton – Segmentation Network) for the automatic segmentation of different retinal lesions simultaneously along with the optic disc. In the proposed MRT‐SegNet, each encoder block consists of an MRT filter bank that extracts the textural feature maps of the retinal images and then fuses them with the local feature maps that are extracted from the traditional encoder block of the network. This fusion enables the network to segment the minute lesions from the retinal surface. The proposed model is evaluated on the IDRiD dataset and achieves a mean Area Under the Precision & Recall Curve (mAUC_PR) of 0.698 and AUC_PR scores of 0.495, 0.706, 0.823, 0.769 for microaneurysms, hemorrhages, hard exudates, and soft exudates respectively. The experimental results demonstrate that the MRT‐SegNet outperformed other multi retinal lesion segmentation models by achieving superior performance.

Prakash Kumar Karn

Papers

Robust retinal blood vessel segmentation using hybrid active contour model

Robust segmentation of exudates from retinal surface using M-CapsNet via EM routing

Time‐frequency analysis and classification of power signals using adaptive cuckoo search algorithm

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

A deeply supervised maximum response texton based SegNet for simultaneous multi retinal lesion segmentation