Shannon Information in K-records for Pareto-type Distributions

Zamani, Zohreh; Madadi, Mohsen

doi:10.2991/jsta.2018.17.3.3

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In addition, based on human visual characteristics [30], the SIE is used to evaluate the denoising effect of the proposed method. Meanwhile, the value of Shannon information entropy [31] is smaller, the denoising effect is better [32]. SIE is defined as Eq.…”

Section: Measurementioning

confidence: 99%

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Hou¹,

Jia²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is enhanced using the SSR algorithm. In addition, based on the extracted edge of CT images using improved Multiscale morphology, a particle swarm optimization (PSO) algorithm is introduced to binarize the image by automatically getting the optimal threshold. To evaluate our method, we use images from three datasets, namely COVID-19, Kaggle-COVID-19, and COVID-Chestxray, respectively. The average values of results are worthy of reference, with the Shannon information entropy of 1.8539, the Precision of 0.9992, the Recall of 0.8224, the F-Score of 1.9158, running time of 11.3000. Finally, three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm. Compared with the other four algorithms, the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

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Section: Measurementioning

confidence: 99%

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Hou¹,

Jia²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Quantile-based entropy function in past lifetime for order statistics and its properties

Zamani,

Madadi

2023

Filomat

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In this paper, we introduce a quantile version of past entropy for order statistics and study some of its properties. It is shown that this measure uniquely determine the quantile function. Two nonparametric classes of distributions are also defined based on the proposed measure. The closure of these classes under increasing convex (concave) transformations and weighted variables are discussed. Moreover, a new stochastic order based on this measure is defined and some features of it are investigated. We give desirable conditions for a function of a random variable to have more quantile past entropy for order statistics than original random variable.

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Shannon Information in K-records for Pareto-type Distributions

Cited by 2 publications

References 13 publications

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Quantile-based entropy function in past lifetime for order statistics and its properties

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