Artery/vein classification of retinal blood vessels using feature selection

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 Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background.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.

Review of Machine Learning Applications Using Retinal Fundus Images

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

Differential artery-vein analysis in quantitative retinal imaging: a review

Quantitative retinal imaging is essential for eye disease detection, staging classification, and treatment assessment. It is known that different eye diseases or severity stages can affect the artery and vein systems in different ways. Therefore, differential artery-vein (AV) analysis can improve the performance of quantitative retinal imaging. In this article, we provide a brief summary of technical rationales and clinical applications of differential AV analysis in fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA).

The Use of Texture Features to Extract and Analyze Useful Information from Retinal Images

Background:

The analysis of retinal images can help to detect retinal abnormalities that are caused by cardiovascular and retinal disorders.

Objective:

In this paper, we propose methods based on texture features for mining and analyzing information from retinal images.

Methods:

The recognition of the retinal mask region is a prerequisite for retinal image processing. However, there is no way to automatically recognize the retinal region. By quantifying and analyzing texture features, a method is proposed to automatically identify the retinal region. The boundary of the circular retinal region is detected based on the image texture contrast feature, followed by the filling of the closed circular area, and then the detected circular retinal mask region can be obtained.

Results:

The experimental results show that the method based on the image contrast feature can be used to detect the retinal region automatically. The average accuracy of retinal mask region detection of images from the Digital Retinal Images for Vessel Extraction (DRIVE) database was 99.34%.

Conclusion:

This is the first time these texture features of retinal images are analyzed, and texture features are used to recognize the circular retinal region automatically.

Artery/vein classification of retinal vessels using classifiers fusion

The morphological changes in retinal blood vessels indicate cardiovascular diseases and consequently those diseases lead to ocular complications such as Hypertensive Retinopathy. One of the significant clinical findings related to this ocular abnormality is alteration of width of vessel. The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents an important approach to solve this problem by means of feature ranking strategies and multiple classifiers decision-combination scheme that is specifically adapted for artery/vein classification. For this, three databases are used with a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies, achieving promising classification performance, with an over all accuracy of 90.45%, 93.90% and 87.82%, in retinal blood vessel separation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

A computational framework to investigate retinal haemodynamics and tissue stress

The process of vision begins in the retina, yet the role of biomechanical forces in the retina is relatively unknown and only recently being explored. This contribution describes a computational framework involving 3D fluid-structure interaction simulations derived from fundus images that work towards creating unique data on retinal biomechanics. We developed methods to convert 2D fundus photographs into 3D geometries that follow the curvature of the retina. Retina arterioles are embedded into a six-layer representation of the retinal tissue with varying material properties throughout the retinal tissue. Using three different human retinas (healthy, glaucoma, diabetic retinopathy) and by varying our simulation approaches, we report the effects of transient versus steady flow, viscosity assumptions (Newtonian, non-Newtonian and Fåhræus-Lindqvist effect) and rigid versus compliant retinal tissue, on resulting wall shear stress (WSS) and von Mises stress. In the retinal arterioles, the choice of viscosity model is important and WSS obtained from models with the Fåhræus-Lindqvist effect is markedly different from Newtonian and non-Newtonian models. We found little difference in WSS between steady-state and pulsatile simulations (< 5%) and show that WSS varies by about 7% between rigid and deformable models. Comparing the three geometries, we found notably different WSS in the healthy (3.3 ± 1.3 Pa), glaucoma (5.7 ± 1.6 Pa) and diabetic retinopathy cases (4.3 ± 1.1 Pa). Conversely, von Mises stress was similar in each case. We have reported a novel biomechanical framework to explore the stresses in the retina. Despite current limitations and lack of complete subject-specific physiological inputs, we believe our framework is the first of its kind and with further improvements could be useful to better understand the biomechanics of the retina.