A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images

A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures

Background: Retinal blood vessel segmentation plays an important role in diagnosing retinal diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. Accurate segmentation of blood vessels in retinal images presents a challenging task due to noise, low contrast, and the complex morphology of blood vessel structures. Methods: In this study, we propose a novel ensemble learning framework combining four deep learning architectures: U-Net, ResNet50, U-Net with a ResNet50 backbone, and U-Net with a transformer block. Each architecture is customized to enhance feature extraction and segmentation performance. The models are trained on the DRIVE and STARE datasets to improve the degree of generalization and evaluated using the performance metric accuracy, F1-Score, sensitivity, specificity, and AUC. Results: The ensemble meta-model integrates predictions from these architectures using a stacking approach, achieving state-of-the-art performance with an accuracy of 0.9778, an AUC of 0.9912, and an F1-Score of 0.8231. These results demonstrate the performance of the proposed technique in identifying thin retinal blood vessels. Conclusions: A comparative analysis using qualitative and quantitative results with individual models highlights the robustness of the ensemble framework, especially under conditions of noise and poor visibility.

An effective vessel segmentation method using SLOA-HGC

Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge texture clarity, and normalizes vessel distribution. It stabilizes neural network training for complex retinal vascular features. Channel-aware self-attention (CAS) improves microfine vessel segmentation sensitivity. Heterogeneous adaptive pooling (HAP) facilitates accurate vessel edge segmentation through multi-scale feature extraction. The ghost fully convolutional Rectified Linear Unit (GFCReLU) module in the output convolutional layer captures deep semantic information for better vessel localization. Optimization training with Sparrow-Integrated Lion Optimization Algorithm (SLOA) employs sparrow stochastic updating and annealing to fine-tune parameters. The results of the experiments on our homemade dataset and three public datasets are as follows: Mean Intersection over Union (MIoU) of 80.61%, 76.14%, 76.90%, 74.11%; Dice coefficients of 78.97%, 72.51%, 72.84%, 68.93%; and accuracies of 94.83%, 95.74%, 96.67%, 95.81% respectively. The model effectively segments retinal blood vessels, offering potential for diagnosing ophthalmic diseases. Our dataset is available at https://github.com/ZhouGuoXiong/Retinal-blood-vessels-for-segmentation .

Analytical Tensor Voting in ND Space and its Properties

This article aims to propose a novel Analytical Tensor Voting (ATV) mechanism, which enables robust perceptual grouping and salient information extraction for noisy N-dimensional (ND) data. Firstly, the approximation of the decaying function is investigated and adopted based on the idea of penalizing the 1-tensor votes by distance and curvature, respectively, followed by the derivation of analytical solution to the 1-tensor voting in ND space from the geometric view. Secondly, a novel spherical representation mechanism is proposed to facilitate the representation of the elementary tensors in various dimensional spaces, where the high dimensional spherical coordinate system is utilized to construct the controllable unit vectors and corresponding 1-tensors. Accordingly, any elementary K-tensor is represented by the surface integration of the constructed 1-tensors over the unit K-sphere. Thirdly, the ATV mechanism is constructed using the adopted decaying function and proposed spherical representation mechanism, where the analytical solution to tensor voting in ND space is derived, which enables the robust and accurate salient information extraction from noisy ND data. Finally, several interesting properties of the proposed ATV mechanism are investigated. Experimental results on synthetic and real data validate the effectiveness, efficiency and robustness of the proposed method in perceptual grouping tasks in 3D,10D or higher dimensional spaces.

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.

Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?

Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively.

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient's Fundus Region Detection Using a Convolutional Neural Network

Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients' fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.

UMS-Rep: Unified modality-specific representation for efficient medical image analysis

Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because it involves unnecessary training repetitions, demands greater computational resources, and requires a relatively large amount of labeled data. In this paper, we propose a multi-task training approach for medical image analysis, where individual tasks are fine-tuned simultaneously through relevant knowledge transfer using a unified modality-specific feature representation (UMS-Rep). We explore different fine-tuning strategies to demonstrate the impact of the strategy on the performance of target medical image tasks. We experiment with different visual tasks (e.g., image denoising, segmentation, and classification) to highlight the advantages offered with our approach for two imaging modalities, chest X-ray and Doppler echocardiography. Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance. Specifically, the proposed approach improves accuracy (up to ∼ 9% ↑) and decreases computational time (up to ∼ 86% ↓) as compared to the baseline approach. Further, our results prove that the performance of target tasks in medical images is highly influenced by the utilized fine-tuning strategy.

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for vessel segmentation and classification for fundus camera images.

Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema- A Review

Human eye is one of the important organs in human body, with iris, pupil, sclera, cornea, lens, retina and optic nerve. Many important eye diseases as well as systemic diseases manifest themselves in the retina. The most widespread causes of blindness in the industrialized world are glaucoma, Age Related Macular Degeneration (ARMD), Diabetic Retinopathy (DR) and Diabetic Macula Edema (DME). The development of a retinal image analysis system is a demanding research topic for early detection, progression analysis and diagnosis of eye diseases. Early diagnosis and treatment of retinal diseases are essential to prevent vision loss. The huge and growing number of retinal disease affected patients, cost of current hospital-based detection methods (by eye care specialists) and scarcity in the number of ophthalmologists are the barriers to achieve the recommended screening compliance in the patient who is at the risk of retinal diseases. Developing an automated system which uses pattern recognition, computer vision and machine learning to diagnose retinal diseases is a potential solution to this problem. Damage to the tiny blood vessels in the retina in the posterior part of the eye due to diabetes is named as DR. Diabetes is a disease which occurs when the pancreas does not secrete enough insulin or the body does not utilize it properly. This disease slowly affects the circulatory system including that of the retina. As diabetes intensifies, the vision of a patient may start deteriorating and leading to DR. The retinal landmarks like OD and blood vessels, white lesions and red lesions are segmented to develop automated screening system for DR. DME is an advanced symptom of DR that can lead to irreversible vision loss. DME is a general term defined as retinal thickening or exudates present within 2 disk diameter of the fovea center; it can either focal or diffuse DME in distribution. In this paper, review the algorithms used in diagnosis of DR and DME.

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

Extraction of Retinal Blood Vessels on Fundus Images by Kirsch's Template and Fuzzy C-Means

Purpose:

Accurate segmentation of retinal blood vessel is an important task in computer-aided diagnosis and surgery planning of diabetic retinopathy. Despite the high-resolution of photographs in fundus photography, the contrast between the blood vessels and the retinal background tends to be poor.

Materials and Methods:

In this proposed method, contrast-limited adaptive histogram equalization is used for noise cancellation and improving the local contrast of the image. By uniform distribution of gray values, it enhances the image and makes the hidden features more visible. The extraction of the retinal blood vessel depends on two levels of optimization. The first level is the extraction of blood vessels from the retinal image using Kirsch's templates. The second level is used to find the coarse vessels with the assistance of the unsupervised method of Fuzzy C-Means clustering. After segmentation, to remove the optic disc, the region-based active contour method is used. The proposed system is evaluated using DRIVE dataset with 40 images.

Results:

The performance of the proposed approach is comparable with state of the art techniques. The proposed technique outperforms the existing techniques by achieving an accuracy of 99.55%, sensitivity of 71.83%, and specificity of 99.86% in the experimental setup.

Conclusion:

The results show that this approach is a suitable alternative technique for the supervised method and it is support for similar fundus images dataset.

Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and road reconstruction. We use a multiscale segmentation algorithm to segment the images, and feature extraction to get the initial road. The fast marching method (FMM) algorithm is employed to obtain the boundary distance field and the source distance field, and the branch backing-tracking method is used to acquire the initial centerline. Road width of each initial centerline is calculated by combining the boundary distance fields, before a tensor field is applied for connecting the broken centerline to gain the final centerline. The final centerline is matched with its road width when the final road is reconstructed. Three experimental results show that the proposed method improves the accuracy of the centerline and solves the problem of broken centerline, and that the method reconstructing the roads is excellent for maintain their integrity.

A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogeneous anatomical structures.

Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions.

Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of cataract

BACKGROUND AND OBJECTIVES

Retinal fundus images are extensively used in manually or without human intervention to identify and analyze various diseases. Due to the comprehensive imaging arrangement, there is a large radiance, reflectance and contrast inconsistency within and across images.

METHOD

A novel method is proposed based on the cataract physical model to reduce the generated blurriness of the fundus image at the time of image acquisition through the thin layer of cataract by the fundus camera. After the blurriness reduction the method is proposed the enhancement procedure of the images with an objective on contrast perfection with no preamble of artifacts. Due to the uneven distribution of thickness of the cataract, the cataract surroundings are first predicted in the domain of frequency. Second, the resultant image of first step enhanced by the intensity histogram equalization in the adapted Hue Saturation Intensity (HSI) color image space such as the gamut problem can be avoided. The concluding image with suitable color and disparity is acquired by using the proposed max-min color correction approach.

RESULTS

The result indicates that not only the proposed method can more effectively enhanced the non-uniform image of retina obtain through thin layer of cataract, but also the resulting image show appropriate brightness and saturation and maintain complete color space information. The projected enhancement method has been tested on the openly available datasets and the result evaluated with the standard used image enhancement algorithms and the cataract removal method. Results show noticeable development over existing methods.

CONCLUSIONS

Cataract often prevents the clinician from objectively evaluating fundus feature. Cataract also affect subjective test. Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of Cataract has shown here to be potentially beneficial.