Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Neutral coding - A report based on an NRP work session

Neural coding by impulses and trains on single and multiple channels, and representation of information in nonimpulse carrier

Optical Character Recognition Using Morphological Attributes.

This dissertation addresses a fundamental computational strategy in image processing hand written English characters using traditional parallel computers. Image acquisition and processing is becoming a thriving industry because of the frequent availability of fax machines, video digitizers, flat-bed scanners, hand scanners, color scanners, and other image input devices that are now accessible to everyone. Optical Character Recognition (OCR) research increased as the technology for a robust OCR system became realistic. There is no commercial effective recognition system that is able to translate raw digital images of hand written text into pure ASCII. The reason is that a digital image comprises of a vast number of pixels. The traditional approach of processing the huge collection of pixel information is quite slow and cumbersome. In this dissertation we developed an approach and theory for a fast robust OCR system for images of hand written characters using morphological attribute features that are expected by the alphabet character set. By extracting specific morphological attributes from the scanned image, the dynamic OCR system is able to generalize and approximate similar images. This generalization is achieved with the usage of fuzzy logic and neural network. Since the main requirement for a commercially effective OCR is a fast and a high recognition rate system, the approach taken in this research is to shift the recognition computation into the system\u27s architecture and its learning phase. The recognition process constituted mainly simple integer computation, a preferred computation on digital computers. In essence, the system maintains the attribute envelope boundary upon which each English character could fall under. This boundary is based on extreme attributes extracted from images introduced to the system beforehand. The theory was implemented both on a SIMD-MC\sp2 and a SISD machine. The resultant system proved to be a fast robust dynamic system, given that a suitable learning had taken place. The principle contributions of this dissertation are: (1) Improving existing thinning algorithms for image preprocessing. (2) Development of an on-line cluster partitioning procedure for region oriented segmentation. (3) Expansion of a fuzzy knowledge base theory to maintain morphological attributes on digital computers. (4) Dynamic Fuzzy learning/recognition technique

Analysis and Synthesis of the Dynamic Response of Retinal Neurons

theory of linear systems analysis is developed in a form directly applicable to the treatment of the Limulus retina. The dynamics of the retina may conveniently be characterized by means of a spatiotemporal transfer function, which summarizes the response of the system to moving sinusoidal gratings ( analysis ). The response of the retina to an arbitrary stimulus may then be calculated by addition of the response to suitably weighted sinusoidal stimuli ( synthesis ). Responses were obtained from the in-situ retina by means of extracellular recording of impulse activity in single optic nerve fibers. Test ommatidia were chosen in the interior of the retina, to avoid asymmetries introduced by the edge of the retina. Stimuli which varied in both space and time were produced under computer control on the screen of a display oscilloscope, and were conveyed to the Limulus eye by means of a fiber-optic taper. Transfer functions were measured using counterphase modulation of cosine gratings according to a sum-of-sinusoids temporal signal, a procedure equivalent to the use of moving gratings, for ommatidia with symmetrical receptive fields. By means of these transfer functions, the responses of the Limulus eye to visual stimuli moving at various velocities were predicted in a parameter-free Fourier synthesis calculation. There was good agreement between these predictions and the measured responses to these stimuli. A quantitative model for the dynamic, integrative action of the Limulus retina is developed, based on the original formulation for the steady state given by the Hartline- Ratliff equations. The model comprises an excitatory generator potential, and dynamic processes of self and lateral inhibition. An explicit expression for the spatiotemporal transfer function is obtained in terms of transfer functions for the generator potential, self-inhibitory, and lateral-inhibitory transductions, and spatial transforms of the lateral inhibitory kernel and the point-spread characteristic of the experimental and physiological optics. Explicit functional forms for these component transductions are adopted. The parameters which occur in these expressions serve to incorporate information about the subcellular physiology of retinal neurons into the quantitative description of the function of the retina as a whole. Procedures are described for the estimation of these parameters from empirical transfer function data. Transfer functions calculated from the model on the basis of parameters obtained with these procedures show good agreement with the corresponding empirical transfer functions. The parameter values obtained in this way are, in general, quite consistent with the results of many more direct (and frequently more invasive) measurements reported in the literature. In particular, the inhibitory kernel, as determined from our transfer function measurements, shows a small crater in the vicinity of the test-ommatidium. The dynamical model can be used to describe the response of the retina in the vicinity of its boundary, as well as in the interior. An analysis, based on the Wiener-Hopf technique, is given for the response of peripheral retinal neurons. The predictions derived from this theory were compared with experiment through the use of illumination patterns in which one half of the retina was kept in darkness, while the remaining half was presented with a moving stimulus. This procedure permitted the calibration of model transfer functions by means of methods appropriate only for interior ommatidia, while simulating the neural environment at the edge of a homogeneous retina. Significant differences between the responses to stimuli which moved toward and away from the simulated edge were observed experimentally, in good agreement with the predictions of the theory. Similar behavior was also observed at the actual anatomical boundary of the eye

Visual recognition of objects : behavioral, computational, and neurobiological aspects

I surveyed work on visual object recognition and perception. In animals, vision has been studied mainly on the behavioral and neurobiological levels. Behavioral data typically show what the visual system, by itself or together with the rest of the organism, is capable of. They show, for example, that humans can recognie objects regardless of size and position, but that rotated objects pose problems. Important insights into the organization of behavior have also been provided by people who suffered localized brain damage. We have learned that the brain is divided into areas subserving different and relatively well-defined behaviors. The visual system itself is also organized in different subsystems; the visual cortex alone contains nearly twenty maps of the visual field. And individual neurons respond selectively to visual stimuli, e.g., the orientation of line segments, color, direction of motion, and, most intriguingly, faces. The question is how the actions of all these neurons produce the behavior we observe. How do neurons represent the shape of objects such that they can be recognized? Before we can answer the question, we have to understand the computational aspect of shape representation, the nature of the problem as it were. Many methods for representing shape have been explored, mainly by computer scientists, but so far no satisfactory answers have been found

Retina-Inspired and Physically Based Image Enhancement

Images and videos with good lightness and contrast are vital in several applications, where human experts make an important decision based on the imaging information, such as medical, security, and remote sensing applications. The well-known image enhancement methods include spatial and frequency enhancement techniques such as linear transformation, gamma correction, contrast stretching, histogram equalization and homomorphic filtering. Those conventional techniques are easy to implement but do not recover the exact colour of the images; hence they have limited application areas. Conventional image/video enhancement methods have been widely used with their different advantages and drawbacks; since the last century, there has been increased interest in retina-inspired techniques, e.g., Retinex and Cellular Neural Networks (CNN) as they attempt to mimic the human retina. Despite considerable advances in computer vision techniques, the human eye and visual cortex by far supersede the performance of state-of-the-art algorithms. This research aims to propose a retinal network computational model for image enhancement that mimics retinal layers, targeting the interconnectivity between the Bipolar receptive field and the Ganglion receptive field. The research started by enhancing two state-of-the-art image enhancement methods through their integration with image formation models. In particular, physics-based features (e.g. Spectral Power Distribution of the dominant illuminate in the scene and the Surface Spectral Reflectance of the objects contained in the image are estimated and used as inputs for the enhanced methods). The results show that the proposed technique can adapt to scene variations such as a change in illumination, scene structure, camera position and shadowing. It gives superior performance over the original model. The research has successfully proposed a novel Ganglion Receptive Field (GRF) computational model for image enhancement. Instead of considering only the interactions between each pixel and its surroundings within a single colour layer, the proposed framework introduces the interaction between different colour layers to mimic the retinal neural process; to better mimic the centre-surround retinal receptive field concept, different photoreceptors' outputs are combined. Additionally, this thesis proposed a new contrast enhancement method based on Weber's Law. The objective evaluation shows the superiority of the proposed Ganglion Receptive Field (GRF) method over state-of-the-art methods. The contrast restored image generated by the GRF method achieved the highest performance in contrast enhancement and luminance restoration; however, it achieved less performance in structure preservation, which confirms the physiological studies that observe the same behaviour from the human visual system

Automatic lineament analysis techniques for remotely sensed imagery

Imperial Users onl