Computer Vision and Machine Learning for Glaucoma Detection

Deep learning based on computer vision and machine learning is an emerging technology in both the medical imaging industry and academia. Despite the existence of some commercial glaucoma detection systems such as retinal imaging, OCT scans, and ocular tonometry, we are at the beginning of a long research pathway toward the future generation of intelligent glaucoma detection systems. Early glaucoma diagnosis prevents permanent structural optic nerve damage and consequent irreversible vision impairment. Longitudinal studies have described both baselines structural and functional factors that predict the development of glaucomatous change in ocular hypertensive and glaucoma suspects. Although there is neither a gold standard for disease diagnosis nor progression, photographic assessment of the optic nerve head remains a mainstay in the diagnosis and management of glaucoma suspects and glaucoma patients. This thesis discusses several image processing techniques comprising disparity map, superpixel and noise removal for pre-processing. A stack of traditional classifiers was utilized as a hybrid model based on the ensemble method to generalise and boost the performance of the proposed model to detect glaucoma through the thickness of the retina. A method was needed aiming at both detecting pathologic changes characteristic of glaucomatous optic neuropathy in optic disc images, and classification of images into categories of glaucomatous/suspect or normal optic discs. Therefore, different machine learning algorithms were used including transfer learning, deep convolutional neural networks, and deep multilayer neural networks that extract features automatically based on clinically relevant optic disc features. Meanwhile, biomarkers were demonstrated with the proposed deep learning model to interpret which parts of the retina had been affected by glaucoma. Finally, this research proposes methods based on evolving deep pre-trained learning architecture, stereo matching with the usage of disparity maps, hybrid models with statistical analysis to retinal nerve fibre layer (RNFL) classification, and visualization of biomarkers with deep learning to detect glaucoma in early stages based on fundus images. Besides, in Appendix A; we discuss a hypothesis of glaucoma detection through detecting specified pattern with signal processing and video processing to achieve glaucoma detection at its early stages. Thus, we are going to specify the OKN pattern of eye movement to detect glaucoma at its initial stage

A New System for Measuring Auto-Fluorescence Changes in Neovascular-AMD after Intravitreal Injection of Bavecizumab

Age-Related Macular Degeneration (AMD) is the second disease diabetes which causes blindness in aged people. The only remedy for AMD is intravenous injection of bavecizumab. To prove the efficiency of remedy, the degenerated cells in Macula should be measured. In this article, a modern system is introduced to measure Auto-Fluorescence in Macula part of retina in order to obtain number of degenerated cells. The system consists of three main parts: Pre-processing stage is omission of margins and reversion of images in retina. Analysis stage is in charge of classification of images and elicitation of their features. In classification the target areas are identified by methods like morphology, dynamic threshold and connected comportments and the features of target area including Euclidean distance to the center of image and density. In the stage of understanding by gathering the features of each class, we will get the measurable parameter of evaluating Auto Fluorescence by the help of which we can count the number of degenerated cells of Macula area. The results are coming from statistical analysis, including linear regression and correlation of data. Experiments have been done on a database of 34 retina images of AMD patients. The average statistical error rate is equal to76 percent. In clinical reviews, the founded relation to disinflation of Macula has been proved, while there were no proved relations to the vision decreasing or increasing of patients