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1

Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform

Patel Rajneesh Kumar, Kashyap Manish

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 3

829--841

EN

TheCOVID-19 epidemic has been causing a global problem since December 2019.COVID-19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is essential. The progression of COVID-19 lung illness has been demonstrated to be aided by chest imaging. The respiratory system is the most vulnerable component of the human body to the COVID virus. COVID can be diagnosed promptly and accurately using images from a chest X-ray and a computed tomography scan. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist venous entry, and pinpoint any new heart problems. The traditional and trending tools are physical, time-inefficient, and not more accurate. Many techniques for detecting COVID utilizing CT scan images have recently been developed, yet none of them can efficiently detect COVID at an early stage. We proposed a two-dimensional Flexible analytical wavelet transform (FAWT) based on a novel technique in this work. This method is decomposed pre-processed images into sub-bands. Then statistical-based relevant features are extracted, and principal component analysis (PCA) is used to identify robust features. After that, robust features are ranked with the help of the Student’s t-value algorithm. Finally, features are applied to Least Square-SVM (RBF) for classification. According to the experimental outcomes, our model beat state-of-the-art approaches for COVID classification. This model attained better classification accuracy of 93.47%, specificity 93.34%, sensitivity 93.6% and F1-score 0.93 using tenfold cross-validation.

2

An efficient algorithm for adaptive total variation based image decomposition and restoration

Liu X., Huang L.

International Journal of Applied Mathematics and Computer Science

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2014

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Vol. 24, no. 2

405--415

EN

With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H-1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm—the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.

3

Wavelet-like decomposition based on filtering in domains of discrete trigonometric transforms : case study

Korohoda R., Dąbrowski A.

Elektronika : konstrukcje, technologie, zastosowania

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2011

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Vol. 52, nr 5

85-88

EN

In this paper a new wavelet-like decomposition-reconstruction scheme of signals is proposed and experimentally verified. Instead of typical filters, i.e., those referring to the Fourier representation, half-band filters based on the discrete trigonometric transforms (DTTs) are used. A comparison with the standard approach has been carried out and the differences are indicated. A perfect reconstruction condition is formulated for the considered filtering systems and illustrated with an example. The comparative study is supported by two examples of one level 2D image decomposition.

PL

W tym artykule przedstawiono zweryfikowaną doświadczalnie propozycję nowego zafalowaniowo- (falkowo-) podobnego schematu dekompozycji-rekonstrukcji sygnałów. W miejsce typowych filtrów, tzn. związanych z reprezentacją Fouriera, zastosowano filtry półpasmowe oparte na dyskretnych transformacjach trygonometrycznych (ang. DTTs). Wyniki porównano z ujęciem klasycznym oraz wskazano różnice. Sformułowano warunek doskonałej rekonstrukcji dla przypadku rozważanych filtrów i zilustrowano go przykładem. Studium porównawcze zostało poparte dwoma przykładami jednostopniowej dwuwymiarowej (2D) dekompozycji obrazu.

4

Porównanie jakości radiometrycznej zdjęć lotniczych wykonanych kamerą analogową i cyfrową

Pyka K.

Archiwum Fotogrametrii, Kartografii i Teledetekcji

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2008

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Vol. 18b

537--545

PL

Dla potrzeb badań zebrano materiał składający się z par zdjęć zarejestrowanych kamerą analogową (LMK1000) i cyfrową (DMC). Było to zdjęcia wyselekcjonowane z wykonanych uprzednio prac fotolotniczych, przy czym materiał dobierano tak, aby w miarę możliwości porównywać zdjęcia wykonane w podobnym okresie wegetacyjnym i posiadające zbliżoną rozdzielczość geometryczną. Na zdjęciach wybierano fragmenty o jednolitym użytkowaniu terenu, np. budynki, parkingi, pola, lasy. W ten sposób zgromadzono materiał badawczy liczący 25 par obrazów o rozmiarach 1024 x 1024 pikseli. Jako metodę badań jakości radiometrycznej obrazów wybrano analizę ich transformat falkowych. Na podstawie analizy równania zachowania wariancji względnej stwierdzono następujące prawidłowości: (1) w obrazach z kamery cyfrowej względna wariancja detali sukcesywnie rośnie wraz ze wzrostem poziomu dekompozycji, (2) w obrazach z kamery analogowej względna wariancja maleje pomiędzy 1. i 2. poziomem rozdzielczości a potem powoli rośnie lub jest stabilna. Sukcesywny wzrost wariancji detali, obserwowany dla obrazów z kamery cyfrowej, świadczy o bardzo niskim poziomie szumów przypadkowych. Z kolei niestabilne zmiany wariancji dla obrazów z kamery analogowej dowodzą wysokiej zawartości szumów.

EN

A set of aerial images taken by two cameras, analogue (LMK 1000) and digital photogrammetric (DMC), was used to compare radiometric quality of images obtained. Pairs consisting of analogue and digital images showing corresponding fragments of identical contents were selected for comparisons. As far as possible, fragments showing homogenous land use, e.g. buildings, parking lots, fields, forests, were selected, which made it possible to observe how the land use affected the wavelet transform. A total of 25 and 9 fragments of medium- and small-scale images, respectively were subjected to comparisons. The fragments selected were 1024 * 1024 pixels in size. The wavelet transformation was chosen as a method with which to compare radiometric image quality. Analysis of the equation for relative image variance preservation allowed to reveal the following patterns: (1) the relative variance of details in digital camera images was found to increase with the decomposition level, (2) the relative variance in analogue camera images was observed to decrease between the first and the second decomposition level. In all the cases examined, the digital camera produced better parameters of noise evaluation. The DMC images contained several times less random noise than those taken with the analogue camera. The study confirmed that it was possible to define the noise content indicators by analysing the wavelet detail coefficients.

5

Application of grade methods to medical data: new examples

Pleszczyńska E.

Biocybernetics and Biomedical Engineering

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2007

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Vol. 27, no. 3

77-93

EN

Data exploration and visualization based on the Grade Correspondence Analysis is waiting for the final recognition by the statistical community. Its main points will be briefly summarized in the Introduction and Section 2 with reference to the contemporary use of Gini-Lorenz concepts and to contemporary visual approaches in analyzing large multivariate datasets. The summary will be illustrated by three recent applications to medical data: Eurostat data on self-perceived health, questionnaire data from the Children’s Memorial Health Institute in Poland and a set of data specially invented to describe NMR human brain image.