Automated Image Detection of Retinal Pathology / Edition 1

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Overview

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.

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Product Details

  • ISBN-13: 9780849375569
  • Publisher: CRC Press
  • Publication date: 10/14/2009
  • Edition number: 1
  • Pages: 384
  • Product dimensions: 6.10 (w) x 9.10 (h) x 1.00 (d)

Meet the Author

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

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

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Table of Contents

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

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