Application of local binary pattern and human visual Fibonacci texture features for classification different medical images

Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns

“…Table I Literature review for the chest x-ray COVID-19 detection as an LBP operator, which is another textural descriptor, (b) it is insensitive to the illumination changes, (c) it is insensitive to noise because of the utilization of threshold value in the binarization process, (d) it is computationally inexpensiveness due to reduction in feature vector dimensionality for p>0 (Table II), and (e) it has the flexibility to add more information due to lower feature vector for p>0 [35]. However, for a window size larger than 3x3, not all pixels in the given window gets included while computing the Fibonacci-p patterns.…”

“…Fibonacci-p patterns are textural feature descriptors that work very similar to LBP, i.e., they also encode the textural pattern information surrounding every pixel present in an image by assigning appropriate Fibonacci weights to them [35] . However, the difference between LBP and Fibonacci -p patterns is that in the latter, a set threshold value is used for binarizing the mxn neighborhood.…”

“…The threshold value determines the extent of information be incorporated in the patterns, i.e., the higher the threshold value, the less the information becomes incorporated and vice versa [39] . The Fibonacci-p patterns serve the following advantages: (a) when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{p}=0$\end{document} , the weights obtained are similar to LBP, thus, behaving as an LBP operator, which is another textural descriptor, (b) it is insensitive to the illumination changes, (c) it is insensitive to noise because of the utilization of threshold value in the binarization process, (d) it is computationally inexpensiveness due to reduction in feature vector dimensionality for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{p} > 0$\end{document} ( Table II ), and (e) it has the flexibility to add more information due to lower feature vector for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{p} > 0$\end{document} [35] .

Fig.

…”