Abstract

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

Highlights

  • In this chapter, the motivations of this thesis study are first introduced

  • As presentd in these tables, the performance of models are as follows: Accuracy: Random Forest > XGBoost >> Gradient Boosting Machines (GBM) >> Logistic Regression > Support Vector Machines (SVM) Speed: Logistic Regression > XGBoost >> Random Forest >> SVM > GBM The results show that the accuracy of Random Forest and XGBoost are very close to each other: 96.37% vs 95.72%, respectively

  • This table indicates that the accuracy of Random Forest and XGBoost are significantly higher than the other mothods

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Summary

Introduction

The motivations of this thesis study are first introduced. The goals and the specific aims are listed and briefly explained. The contributions of this research are described and, an overview of the thesis is presented. First retina and retinal imaging are briefly explained. Retinal fundus imaging and the methods used to detect OD and OC are reviewed. Retina is a thin layer of tissue on the inside of the eye. It is located near the optic nerve. The purpose of retina is to receive the light that the lens focuses, convert it into neural signals, and send these signals on to the brain for visual recognition [2].

Objectives
Methods
Results
Conclusion

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