Visionary Ophthalmics: Confluence of Computer Vision and Deep Learning for Ophthalmology

Ophthalmology is a medical field ripe with opportunities for meaningful application of computer vision algorithms. The field utilizes data from multiple disparate imaging techniques, ranging from conventional cameras to tomography, comprising a diverse set of computer vision challenges. Computer vision has a rich history of techniques that can adequately meet many of these challenges. However, the field has undergone something of a revolution in recent times as deep learning techniques have sprung into the forefront following advances in GPU hardware. This development raises important questions regarding how to best leverage insights from both modern deep learning approaches and more classical computer vision approaches for a given problem. In this dissertation, we tackle challenging computer vision problems in ophthalmology using methods all across this spectrum. Perhaps our most significant work is a highly successful iris registration algorithm for use in laser eye surgery. This algorithm relies on matching features extracted from the structure tensor and a Gabor wavelet – a classically driven approach that does not utilize modern machine learning. However, drawing on insight from the deep learning revolution, we demonstrate successful application of backpropagation to optimize the registration significantly faster than the alternative of relying on finite differences. Towards the other end of the spectrum, we also present a novel framework for improving RANSAC segmentation algorithms by utilizing a convolutional neural network (CNN) trained on a RANSAC-based loss function. Finally, we apply state-of-the-art deep learning methods to solve the problem of pathological fluid detection in optical coherence tomography images of the human retina, using a novel retina-specific data augmentation technique to greatly expand the data set. Altogether, our work demonstrates benefits of applying a holistic view of computer vision, which leverages deep learning and associated insights without neglecting techniques and insights from the previous era

Personal Identification Based on Live Iris Image Analysis

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Detail Enhancing Denoising of Digitized 3D Models from a Mobile Scanning System

The acquisition process of digitizing a large-scale environment produces an enormous amount of raw geometry data. This data is corrupted by system noise, which leads to 3D surfaces that are not smooth and details that are distorted. Any scanning system has noise associate with the scanning hardware, both digital quantization errors and measurement inaccuracies, but a mobile scanning system has additional system noise introduced by the pose estimation of the hardware during data acquisition. The combined system noise generates data that is not handled well by existing noise reduction and smoothing techniques. This research is focused on enhancing the 3D models acquired by mobile scanning systems used to digitize large-scale environments. These digitization systems combine a variety of sensors – including laser range scanners, video cameras, and pose estimation hardware – on a mobile platform for the quick acquisition of 3D models of real world environments. The data acquired by such systems are extremely noisy, often with significant details being on the same order of magnitude as the system noise. By utilizing a unique 3D signal analysis tool, a denoising algorithm was developed that identifies regions of detail and enhances their geometry, while removing the effects of noise on the overall model. The developed algorithm can be useful for a variety of digitized 3D models, not just those involving mobile scanning systems. The challenges faced in this study were the automatic processing needs of the enhancement algorithm, and the need to fill a hole in the area of 3D model analysis in order to reduce the effect of system noise on the 3D models. In this context, our main contributions are the automation and integration of a data enhancement method not well known to the computer vision community, and the development of a novel 3D signal decomposition and analysis tool. The new technologies featured in this document are intuitive extensions of existing methods to new dimensionality and applications. The totality of the research has been applied towards detail enhancing denoising of scanned data from a mobile range scanning system, and results from both synthetic and real models are presented

Research, implementation and comparison between methods for pupil detection in an image

The objective of this work is to design and implement a pupil detection solution to be used as part of an anti-spoofing mechanism in face recognition. This is intended to improve the security and reliability of face recognition technology by minimizing its risks and, thus, provide confidence to users and companies that employ it. In addition, the project wants to focus on the efficiency and accuracy of the final solution, to do so, various alternatives based on different methods will be developed and compared. To achieve this, the project follows a linear work methodology, that is to say, starting from the search of information, through the theoretical design phase, the code implementation and ending with the presentation of an operational solution. The results achieved after the whole process have been satisfactory and show the viability of the target set. Also, the work provides a series of possible future improvements while reflecting how the initial idea has been evolving and maturing to end up resulting in a product more solid and adjusted to the detected need

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd

DESIGNING EYE TRACKING ALGORITHM FOR PARTNER-ASSISTED EYE SCANNING KEYBOARD FOR PHYSICALLY CHALLENGED PEOPLE

The proposed research work focuses on building a keyboard through designing an algorithm for eye movement detection using the partner-assisted scanning technique. The study covers all stages of gesture recognition, from data acquisition to eye detection and tracking, and finally classification. With the presence of many techniques to implement the gesture recognition stages, the main objective of this research work is implementing the simple and less expensive technique that produces the best possible results with a high level of accuracy. The results, finally, are compared with similar works done recently to prove the efficiency in implementation of the proposed algorithm. The system starts with the calibration phase, where a face detection algorithm is designed to detect the user‟s face by a trained support vector machine. Then, features are extracted, after which tracking of the eyes is possible by skin-colour segmentation. A couple of other operations were performed. The overall system is a keyboard that works by eye movement, through the partner-assisted scanning technique. A good level of accuracy was achieved, and a couple of alternative methods were implemented and compared. This keyboard adds to the research field, with a new and novel combination of techniques for eye detection and tracking. Also, the developed keyboard helps bridge the gap between physical paralysis and leading a normal life. This system can be used as comparison with other proposed algorithms for eye detection, and might be used as a proof for the efficiency of combining a number of different techniques into one algorithm. Also, it strongly supports the effectiveness of machine learning and appearance-based algorithms