Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images

Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis

Diabetic retinopathy (DR) is one of the most common chronic diseases around the world. Early screening and diagnosis of DR patients through retinal fundus is always preferred. However, image screening and diagnosis is a highly time-consuming task for clinicians. So, there is a high need for automatic diagnosis. The objective of our study is to develop and validate a new automated deep learning-based approach for diabetic retinopathy multi-class detection and classification. In this study we evaluate the contribution of the DR features in each color channel then we pick the most significant channels and calculate their principal components (PCA) which are then fed to the deep learning model, and the grading decision is decided based on a majority voting scheme applied to the out of the deep learning model. The developed models were trained on a publicly available dataset with around 80K color fundus images and were tested on our local dataset with around 100 images. Our results show a significant improvement in DR multi-class classification with 85% accuracy, 89% sensitivity, and 96% specificity.

Extraction of Blood Vessels in Retinal Images Using Four Different Techniques

A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique.