Information-Based Analysis of the Relation Between Visual Stimuli and Human Eye Movements

Namazi, Hamidreza; Daneshi, Asieh; Shirzadian, Touraj; Azarnoush, Hamed; Jafari, Sajad

doi:10.1142/s021947751950010x

Cited by 36 publications

(3 citation statements)

References 49 publications

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“…The probability value is calculated by normalizing the total number of pixels for every possible intensity to the total number of pixels. 33 A larger value of Shannon's entropy corresponds to a greater amount of information within an image. 34 We quantify the complexity of images by calculating their fractal dimensions.…”

Section: Methodsmentioning

confidence: 99%

Complexity-Based Classification of the Coronavirus Disease (Covid-19)

Namazi

Kulish

2020

Fractals

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COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon’s entropy and fractal theory. Shannon’s entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

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Section: Methodsmentioning

confidence: 99%

Complexity-Based Classification of the Coronavirus Disease (Covid-19)

Namazi

Kulish

2020

Fractals

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“…Figure 1 b shows the covering by

cubes of size

. Now, the probability

is calculated by first normalising all pixels in each cube by dividing its value by the max intensity [ 56 ]; in our example, each pixel has a value of 0 or 255, and each cube has four pixels. Thus, this process results in the table in Figure 2 a.…”

Section: Preliminariesmentioning

confidence: 99%

An Entropy-Based Measure of Complexity: An Application in Lung-Damage

Ortiz-Vilchis

Ramírez-Arellano

2022

Entropy

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The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.

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“…fractal theory [10][11]) to evaluate nonlinear physiological signals, there have been plenty of works that employed different kinds of entropy. The studies which evaluated voice [12], eye movements [13], heart rate variability (HRV) [14], and Galvanic Skin Response (GSR) signals [15] using different types of entropy can be mentioned.…”

Section: Introductionmentioning

confidence: 99%