IIIT Hyderabad Publications
Biologically inspired methods for Diabetic Retinopathy Detection
Report no: IIIT/TH/2016/47
Diabetic Retinopathy (DR) is an eye-aliment, which is one of the leading causes of blindness all over the world. With timely diagnosis and proper care, it is possible to manage this condition and control its severity. The sooner this condition is diagnosed, the better are the chances of preventing blindness and causing any further structural damage associated with DR. In this thesis, we address the problem of detecting DR from colour retinal images, by employing some biologically inspired approaches in a machine learning setting. DR detection entails the detection of a variety of lesion, among which we restrict our attention to bright lesions such as hard exudates, cotton wool spots and dark lesions such as haemorrhages. Microaneurysms, which are the smallest of all types of retinal lesions are not covered in this study, since due to their extremely small size, they are generally studied and analysed separately. In this thesis, we approach the problem of DR lesion detection via visual saliency, which is a basic and important biologically motivated concept. We show the relevance of visual saliency in bright lesion detection by demonstrating that visual saliency models, do capture hard exudates. We compare three popular visual saliency models, namely, Itti-Koch, Graph Based Visual Saliency (GBVS) and Spectral Residual (SR) and show that top 90% of the most salient locations provided by these models, are able to correspond with an average of 70% of all true hard exudates present in images. We propose a multi-scale version of the SR model, called as Extended SR (ESR) model and show its relative performance to be about 28.7% better than the previous models in detecting true hard exudates. This ESR model is used as a basis to design a bright lesion detector, which provides at the image level, high sensitivity (0.93) and specificity (0.78), averaged over 6 public datasets. We next propose two systems, one aimed as an assistive system for an annotator and another for detecting both bright and dark lesions. Both are based on a biologically inspired novel, lesion enhancing pipeline. The proposed system has been evaluated over 5 public datasets and is found to outperform existing systems with sensitivity/specificity values of 0.92/0.86 for bright lesions and 0.86/0.84 for dark lesions. We also evaluate the regional level lesion detection on one public dataset and obtain a performance figure of (0.91/0.88) for bright lesions and (0.85/0.83) for dark lesions. These underscore the potential of biologically inspired approaches to computer aided diagnostic algorithm development.
Full thesis: pdf
Centre for Visual Information Technology
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