Retinal Image Analysis: A Review

Images of the eye ground or retina not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automated retina image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This can in turn be used to reduce human errors or to provide services to remote areas. In this review paper, we discuss some of the current techniques used to automatically detect the important clinical features of retinal image, such as the blood vessels, optic disc and macula. The quantitative analysis and measurements of these features can be used to better understand the relationship between various diseases and the retinal features

Hemodynamic and morphological vasculature response to a burn monitored using a combined dual-wavelength laser speckle and optical microangiography imaging system

A multi-functional imaging system capable of determining relative changes in blood flow, hemoglobin concentration, and morphological features of the blood vasculature is demonstrated. The system combines two non-invasive imaging techniques, a dual-wavelength laser speckle contrast imaging (2-LSI) and an optical microangiography (OMAG) system. 2-LSI is used to monitor the changes in the dynamic blood flow and the changes in the concentration of oxygenated (HbO), deoxygenated (Hb) and total hemoglobin (HbT). The OMAG system is used to acquire high resolution images of the functional blood vessel network. The vessel area density (VAD) is used to quantify the blood vessel network morphology, specifically the capillary recruitment. The proposed multi-functional system is employed to assess the blood perfusion status from a mouse pinna before and immediately after a burn injury. To our knowledge, this is the first non-invasive, non-contact and multifunctional imaging modality that can simultaneously measure variations of several blood perfusion parameters

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment