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 to 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 thesis 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 heterogenous anatomical structures

Blood vessel detection in retinal images and its application in diabetic retinopathy screening

In this dissertation, I investigated computing algorithms for automated retinal blood vessel detection. Changes in blood vessel structures are important indicators of many diseases such as diabetes, hypertension, etc. Blood vessel is also very useful in tracking of disease progression, and for biometric authentication. In this dissertation, I proposed two algorithms to detect blood vessel maps in retina. The first algorithm is based on integration of a Gaussian tracing scheme and a Gabor-variance filter. This algorithm traces the large blood vessel in retinal images enhanced with adaptive histogram equalization. Small vessels are traced on further enhanced images by a Gabor-variance filter. The second algorithm is called a radial contrast transform (RCT) algorithm, which converts the intensity information in spatial domain to a high dimensional radial contrast domain. Different feature descriptors are designed to improve the speed, sensitivity, and expandability of the vessel detection system. Performances comparison of the two algorithms with those in the literature shows favorable and robust results. Furthermore, a new performance measure based on central line of blood vessels is proposed as an alternative to more reliable assessment of detection schemes for small vessels, because the significant variations at the edges of small vessels need not be considered. The proposed algorithms were successfully tested in the field for early diabetic retinopathy (DR) screening. A highly modular code library to take advantage of the parallel processing power of multi-core computer architecture was tested in a clinical trial. Performance results showed that our scheme can achieve similar or even better performance than human expert readers for detection of micro-aneurysms on difficult images

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

A retinal vasculature tracking system guided by a deep architecture

Many diseases such as diabetic retinopathy (DR) and cardiovascular diseases show their early signs on retinal vasculature. Analysing the vasculature in fundus images may provide a tool for ophthalmologists to diagnose eye-related diseases and to monitor their progression. These analyses may also facilitate the discovery of new relations between changes on retinal vasculature and the existence or progression of related diseases or to validate present relations. In this thesis, a data driven method, namely a Translational Deep Belief Net (a TDBN), is adapted to vasculature segmentation. The segmentation performance of the TDBN on low resolution images was found to be comparable to that of the best-performing methods. Later, this network is used for the implementation of super-resolution for the segmentation of high resolution images. This approach provided an acceleration during segmentation, which relates to down-sampling ratio of an input fundus image. Finally, the TDBN is extended for the generation of probability maps for the existence of vessel parts, namely vessel interior, centreline, boundary and crossing/bifurcation patterns in centrelines. These probability maps are used to guide a probabilistic vasculature tracking system. Although segmentation can provide vasculature existence in a fundus image, it does not give quantifiable measures for vasculature. The latter has more practical value in medical clinics. In the second half of the thesis, a retinal vasculature tracking system is presented. This system uses Particle Filters to describe vessel morphology and topology. Apart from previous studies, the guidance for tracking is provided with the combination of probability maps generated by the TDBN. The experiments on a publicly available dataset, REVIEW, showed that the consistency of vessel widths predicted by the proposed method was better than that obtained from observers. Moreover, very noisy and low contrast vessel boundaries, which were hardly identifiable to the naked eye, were accurately estimated by the proposed tracking system. Also, bifurcation/crossing locations during the course of tracking were detected almost completely. Considering these promising initial results, future work involves analysing the performance of the tracking system on automatic detection of complete vessel networks in fundus images.Open Acces

Retinal image processing for automated detection and grading of diabetic retinopathy

The main eye condition associated with diabetes is called diabetic retinopathy and is, the main cause of blindness. The earliest signs of this disease include damage to retinal blood vessels and then the formation of lesions such as exudates and red spots. Such lesions are normally detected manually by clinicians in intensive and time-consuming processes. Computer:-_aided detection and grading of such conditions could facilitate an immediate and accurate Criagnosis. Whilst some progress has been made to detect these diseases, there is no complete system for automated detection and grading of diabetic retinopathy and this is hindering the development of automated methods to support assessment of diabetic eye disease. The aim of this work is to develop computer algorithms that can be used in the medical screening system for evaluating the condition of the retina leading to successful treatment. This work comprises five stages: 1) image pre-processing, 2) retinal structure extraction; 3) hard exudate detection, 4) red lesion detection and 5) grading of diabetic retinopathy. The aim of image pre-processing is to prepare the image with better quality where shade correction using morphological processes and contrast enhancement using fuzzy logic-based method are applied to the image. In the retinal structure extraction, multi-scale morphological technique and classification procedure are proposed for blood vessel detection. Vasculature loop-based method for the optic disc localisation is proposed, while for fovea localisation, a method based on its features and geometric relationships with the other retinal structures is developed. These methods have the advantage of lower computational complexity and competitive performance compared to the existing related methods. A novel coarse to fine strategy is proposed to detect hard exudates, where a local variation operator is used to calculate the standard deviation around each pixel followed by automated thresholding, morphological operations, and classification to segment coarse hard exudates. To fine-tune the result of coarse hard exudates, two region-based segmentation techniques are investigated to detect fine hard exudates. The significance of this method is manifested by its superior performance, lower computational complexity (compared to the current state of the art) and the ability to deal with a variety of image qualities. A novel red lesion detection method is proposed using mathematical morphology to segment candidate red lesions followed by refining them from traces of retinal structures and then a classification based on red lesion features is used to detect red lesions with high degree of discrimination between genuine red lesions and artifacts and as a result its detection performance has proved to be favourable. Grading of diabetic retinopathy is a very important stage after the detection of retinal lesions to evaluate their severity and to decide appropriate treatment. The most reliable medical approaches to diabetic retinopathy grading were investigated to build a novel computer-aided model for automated grading based on the clinical criteria and results of the earlier lesion segmentation. This model quantifies the nature, extent and spatial distribution of all the detected features and provides a clinical grading assessment. This is among the first of such models published and as such the novelty is considered to be one of the main contributions of this thesis

Mastering Endo-Laparoscopic and Thoracoscopic Surgery

This is an open access book. The book focuses mainly on the surgical technique, OR setup, equipments and devices necessary in minimally invasive surgery (MIS). It serves as a compendium of endolaparoscopic surgical procedures. It is an official publication of the Endoscopic and Laparoscopic Surgeons of Asia (ELSA). The book includes various sections covering basic skills set, devices, equipments, OR setup, procedures by area. Each chapter cover introduction, indications and contraindications, pre-operative patient’s assessment and preparation, OT setup (instrumentation required, patient’s position, etc.), step by step description of surgical procedures, management of complications, post-operative care. It includes original illustrations for better understanding and visualization of specific procedures. The book serves as a practical guide for surgical residents, surgical trainees, surgical fellows, junior surgeons, surgical consultants and anyone interested in MIS. It covers most of the basic and advanced laparoscopic and thoracoscopic surgery procedures meeting the curriculum and examination requirements of the residents