1st Edition

Automated Image Detection of Retinal Pathology

By Herbert Jelinek, Michael J. Cree Copyright 2010
    394 Pages 22 Color & 37 B/W Illustrations
    by CRC Press

    393 Pages 22 Color & 37 B/W Illustrations
    by CRC Press

    Discusses the Effect of Automated Assessment Programs on Health Care Provision

    Diabetes is approaching pandemic numbers, and as an associated complication, diabetic retinopathy is also on the rise. Much about the computer-based diagnosis of this intricate illness has been discovered and proven effective in research labs. But, unfortunately, many of these advances have subsequently failed during transition from the lab to the clinic. So what is the best way to diagnose and treat retinopathy? Automated Image Detection of Retinal Pathology discusses the epidemiology of the disease, proper screening protocols, algorithm development, image processing, and feature analysis applied to the retina.

    Conveys the Need for Widely Implemented Risk-Reduction Programs

    Offering an array of informative examples, this book analyzes the use of automated computer techniques, such as pattern recognition, in analyzing retinal images and detecting diabetic retinopathy and its progression as well as other retinal-based diseases. It also addresses the benefits and challenges of automated health care in the field of ophthalmology. The book then details the increasing practice of telemedicine screening and other advanced applications including arteriolar-venous ratio, which has been shown to be an early indicator of cardiovascular, diabetes, and cerebrovascular risk.

    Although tremendous advances have been made in this complex field, there are still many questions that remain unanswered. This book is a valuable resource for researchers looking to take retinal pathology to that next level of discovery as well as for clinicians and primary health care professionals that aim to utilize automated diagnostics as part of their health care program.

    Introduction

    Why Automated Image Detection of Retinal Pathology?

    Automated Assessment of Retinal Eye Disease

    Diabetic Retinopathy and Public Health

    Introduction

    The pandemic of diabetes and its complications

    Retinal structure and function

    Definition and description

    Classification of Diabetic Retinopathy

    Differential Diagnosis of Diabetic Retinopathy

    Systemic Associations of Diabetic Retinopathy

    Pathogenesis

    Treatment

    Screening

    Conclusion

    Detecting Retinal Pathology Automatically with Special Emphasis on Diabetic Retinopathy

    Historical aside

    Approaches to computer (aided) diagnosis

    Detection of diabetic retinopathy lesions

    Detection of lesions and segmentation of retinal anatomy

    Detection and staging of diabetic retinopathy: pixel to patient

    Directions for research

    Finding a Role for Computer-Aided Early Diagnosis of Diabetic Retinopathy

    Mass Examinations of Eyes in Diabetes

    Developing and Defending a Risk Reduction Programme

    Assessing Accuracy of a Diagnostic Test

    Improving Detection of Diabetic Retinopathy

    Measuring Outcomes of Risk Reduction Programmes

    User Experiences of Computer-Aided Diagnosis

    Planning a Study to Evaluate Accuracy

    Conclusion

    Retinal Markers for Early Detection of Eye Disease

    Abstract

    Introduction

    Non-Proliferative Diabetic Retinopathy

    Chapter Overview

    Related Works on Identification of Retinal Exudates and the Optic Disc

    Preprocessing

    Pixel-Level Exudate Recognition

    Application of Pixel-Level Exudate Recognition on the Whole Retinal Image

    Locating the Optic Disc in Retinal Images

    Conclusion

    Automated Microaneurysm Detection for Screening

    Characteristics of microaneurysms and dot-haemorrhages

    History of Automated Microaneurysm Detection

    Microaneurysm Detection in Colour Retinal Images

    The Waikato Automated Microaneurysm Detector

    Issues for Microaneurysm Detection

    Research Application of Microaneurysm Detection

    Conclusion

    Retinal Vascular Changes as Biomarkers of Systemic Cardiovascular Diseases

    Introduction

    Early Description of Retinal Vascular Changes

    Retinal Vascular Imaging

    Retinal Vascular Changes and Cardiovascular Disease

    Retinal Vascular Changes and Metabolic Diseases

    Retinal Vascular Changes and other Systemic Diseases

    Genetic Associations of Retinal Vascular Changes

    Conclusion

    Segmentation of Retinal Vasculature Using Wavelets and Supervised Classification: Theory and Implementation

    Introduction

    Theoretical Background

    Segmentation Using the 2-D Gabor Wavelet and Supervised Classification

    Implementation and Graphical User Interface

    Experimental Results

    Conclusion

    Determining Retinal Vessel Widths and Detection of Width Changes

    Identifying Blood Vessels

    Vessel Models

    Vessel Extraction Methods

    Can’s Vessel Extraction Algorithm

    Measuring Vessel Width

    Precise Boundary Detection

    Continuous Vessel Models with Spline-based Ribbons

    Estimation of Vessel Boundaries using Snakes

    Vessel Width Change Detection

    Conclusion

    Geometrical and Topological Analysis of Vascular Branches from Fundus Retinal Images

    Introduction

    Geometry of Vessel Segments and Bifurcations

    Vessel Diameter Measurements from Retinal Images

    Clinical Findings from Retinal Vascular Geometry

    Topology of the Vascular Tree

    Automated Segmentation and Analysis of Retinal Fundus Images

    Clinical Findings from Retinal Vascular Topology

    Conclusion

    Tele-Diabetic Retinopathy Screening and Image Based Clinical Decision Support

    Introduction

    Telemedicine

    Telemedicine screening for Diabetic retinopathy

    Image-based clinical decision support systems

    Conclusion

    Biography

    Herbert Jelinek, Charles Stuart University, Albury, New South Wales, Australia

    Michael J. Cree, University of Waikato, Hamilton, New Zealand