Hurst exponent estimation of fractional surfaces for mammogram images analysis

Abstract One of the main concerns about the fast spreading coronavirus disease 2019 (Covid-19) pandemic is how to intervene. We analysed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) isolates data using the multifractal approach and found a rich in viral genome diversity, which could be one of the root causes of the fast Covid-19 pandemic and is strongly affected by pressure and health index of the hosts inhabited regions. The calculated mutation rate (mr) is observed to be maximum at a particular pressure, beyond which mr maintains diversity. Hurst exponent and fractal dimension are found to be optimal at a critical pressure (Pm), whereas, for P > Pm and P < Pm, we found rich genome diversity relating to complicated genome organisation and virulence of the virus. The values of these complexity measurement parameters are found to be increased linearly with health index values.

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Stabilogram diffusion analysis algorithm to estimate the Hurst exponent of high-dimensional fractals

Journal of Statistical Mechanics Theory and Experiment ◽

10.1088/1742-5468/2012/04/p04014 ◽

2012 ◽

Vol 2012 (04) ◽

pp. P04014

Author(s):

Oleg Gorshkov

Keyword(s):

Hurst Exponent ◽

High Dimensional ◽

Analysis Algorithm ◽

Stabilogram Diffusion Analysis ◽

Diffusion Analysis

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Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

International Journal of Image and Graphics ◽

10.1142/s0219467815500011 ◽

2015 ◽

Vol 15 (01) ◽

pp. 1550001 ◽

Cited By ~ 1

Author(s):

A. Suruliandi ◽

G. Murugeswari ◽

P. Arockia Jansi Rani

Keyword(s):

Image Segmentation ◽

Performance Metrics ◽

Empirical Evaluation ◽

Rate Sensitivity ◽

Digital Mammogram ◽

Abnormality Detection ◽

Texture Descriptor ◽

Texture Pattern ◽

Local Texture ◽

Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

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