On identification of human eye retinas by the covariance analysis of their digital images

Jonas Skeivalas,Eimuntas Parseliunas

doi:10.1117/1.oe.52.7.073106

Jonas Skeivalas, Eimuntas Parseliunas

Open Access

https://doi.org/10.1117/1.oe.52.7.073106

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Abstract

An identification of human eye retinas by applying the covariance function and wavelet theory is presented. The estimations of the autocovariance functions of the two digital images or single image are calculated according to random functions, based on the vectors created from the digital image pixels. The estimations of the pixel’s vectors are calculated by spreading the pixel arrays of the digital images into single column. During the changing of the scale of the digital image, the wave frequencies of the colors of the single pixels are prekept, and the influence of the change of a scale in the procedures of the calculations of the covariance functions does not occur. The Red, Green, Blue (RGB) color model of the colors spectrum for the encoding of the digital images was applied. The influence of the RGB spectrum components and the tensor of colors on the estimations of the covariance functions were analyzed. The identity of the digital images is estimated by analysis of the changes of the correlation coefficient values in the corresponding diapason.

Highlights

A number of papers are contributing to retinal imaging and image analysis.[1,2] Retinal researchers and practicians make more and more wide use of the digital images, which causes the need to improve the image processing algorithms
The theoretical model is based on the stationary random function taking into account that the errors of the color wave frequencies are random and that they are of the same accuracy, i.e., a mean of errors is MΔ 1⁄4 const 1⁄4 0 and their dispersion is DΔ 1⁄4 const, and that the covariance function of the digital images depends only on the difference of the arguments, i.e., on the pixel quantize interval
The estimations of the covariance function of the two digital images or the autocovariance function of the single image are calculated according to random functions, based on the vectors, created from the digital image pixels

Summary

Introduction

A number of papers are contributing to retinal imaging and image analysis.[1,2] Retinal researchers and practicians make more and more wide use of the digital images, which causes the need to improve the image processing algorithms. In this paper we stress on an identification of the digital images by applying the photogrammetric methods and random functions theory. The spatial positions of the digital image pixels are defined by the spatial region of the frequencies of color waves, i.e., by radiometric level, applying the Red, Green, Blue (RGB) coding format of the colors spectrum. The main goal of this article is to provide the opportunity for a continuous improvement of the core algorithms, driven by performance of the covariance analysis approach. Such algorithms could be used in various areas of research and practice, including public and clinical health, biomedicine, security systems, etc

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