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Appl Soft Comput. Published online Feb 3. Author information Article notes Copyright and License information Disclaimer. All rights reserved. Elsevier hereby grants permission to make all its COVIDrelated research that is available on the COVID resource centre – including this research content – immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.

Abstract Due to the absence of any specialized drugs, the novel coronavirus disease or COVID is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID patients.

Introduction Automated computer-aided systems prove their effectiveness and real-life applicability in various scenarios. Table 1 Some of the related literatures and their brief overview. The calibrated source-to-background curves are used to determine the volume using the iterative thresholding procedure.

One major drawback of the system is that it cannot effectively measure small volumes. Wiemker et. This work proposes a divergence theorem and histogram-based Ct image segmentation approach.

This approach is can effectively and optimally isolate the lung nodules from the CT scan images. In this context, the optimality is defined in terms of the mean gradient of the iso-surface and the sphericity. Asari et. This algorithm is consisting of two stages where the first stage employs a global thresholding approach and in the second phase, the differential region growing is used to extract the gastrointestinal lumen from the endoscopic images.

The dynamic hill-clustering approach is used to ascertain the effectiveness of the termination criteria and to look after the growth process. Yu-qian et. Traditional gradient-based edge detection approaches are susceptible to noise and therefore, this approach proposes a novel approach to detect edges of the lung CT scan images using mathematical morphology.

This approach is tested on the CT images which are corrupted with the salt-and-pepper noise and its efficiency is proved by comparing this approach with some of the other standard approaches.

It is observed that this approach can efficiently reduce the effect of noise and also can generate precise edges. Falcao et. This approach is highly dependent on the user intervention to efficiently determine the segmented regions and to define the objects.

This approach is found to be 3 to 15 times faster compared to manual tracing. This approach can be applied almost independently to the applications. One main problem associated with this method is the difficulties associated with the choice of slabs and orthogonal slices which has a serious impact on the efficiency of this approach. Pan et. The proposed approach addresses the problem of discontinuous edges and dependency on the initialization which are associated with the traditional edge detection approaches.

In this work, the intensity of the gradient images is modeled as the concentration of the nutrients and the property of the bacteria Escherichia coli. The edges are highlighted as the paths of the bacteria. Although this approach performs well and comparative study shows the effectiveness of the proposed approach still, one problem of this approach is not very robust to noise. Noise can lead to crumpled edges.

This approach is not also suitable to handle overlapped cells. Ji et. Traditional fuzzy C-means clustering approach does not consider the spatial information and less robust to noise.

This work proposes a modification which is known as the weighted image patch-based FCM. In this work, pixels are replaced with the weighted patches which is helpful to incorporate spatial information in the segmentation process.

It is helpful to increase the reliability of the overall segmentation process but it also increases the computational overhead drastically. Agrawal et. This work proposes a novel hybrid approach which is based on the genetic algorithm and the bacterial foraging algorithm. The combination of these two approaches is used to optimize the objective function of the fuzzy c-means clustering.

The final cluster centers are obtained using a method called optimum boundary point detection. This approach cannot determine the optimal number of clusters automatically and produces inaccurate results if the predefined clusters and the actual number of clusters differ. This approach is based on intuitionistic fuzzy set theory and it is known as the intuitionistic fuzzy C means clustering.

In this work, a novel objective function which is known as intuitionistic fuzzy entropy is incorporated with the traditional fuzzy C-means clustering. This approach is applied to different CT scan images to prove its efficiency. Miao et. This approach can be divided into two phases where the first phase incorporates a dictionary learning method to handle the noise.

In the second phase, this dictionary learning approach is hybridized with the Improved fuzzy c-means clustering approach. The proposed approach is not efficient for medical images with inhomogeneous intensity distribution. Open in a separate window. A brief overview of the artificial cell swarm optimization procedure This is a recently developed metaheuristic procedure that is inspired by the artificial cell division procedure.

The incorporated modifications are listed below [47] : i. The artificial cells are not depending on the current state to participate in the cell division process. Fuzzy C-means clustering based on type 2 fuzzy system The proposed approach adopts the type 2 fuzzy logic-based clustering approach to effectively model and handle the random uncertainties. A point with higher uncertainty has a lesser impact on the overall clustering process and vice-versa.

It helps to achieve more realistic results. Proposed method of superpixel computation The ever-growing technology allows us to increase the quality of the image acquisition hardware. Table 3 Details of the CT scan images under test.

Proposed superpixel coupled fuzzy ACSO approach-based segmentation The conventional fuzzy C-means clustering approach often overlooks some important spatial information that can be costly in terms of the segmentation performance. Dataset description CT scan images of the chest region are collected from the COVID positive patients from different geographic regions. Experimental results The experiments are performed in the MatLab Ra on a computer that is equipped with an Intel i3 processor and 4 GB main memory.

Table 4 Performance evaluation of different approaches using Davies—Bouldin index The highlighted values indicates acceptable values. Image Id Algorithm No. Table 5 Performance evaluation of different approaches using Xie—Beni index The highlighted values indicates acceptable values. Table 6 Performance evaluation of different approaches using Dunn index The highlighted values indicates acceptable values. Table 8 Comparison of the proposed approach with the active contour method.

Study of the convergence rate The rate of convergence is an important parameter to be studied. Analysis of the complexity The time complexity is an important aspect that is to be analyzed. Discussion 6. Threats to validity The obtained results indicate that the proposed approach is suitable for real-life scenarios and also performs efficiently.

Limitations Although the proposed approach is efficient enough to segment the CT scan images automatically and produces realistic segmented outcomes still, some important drawbacks can be observed in this proposed approach that can be addressed in the subsequent works. Conclusion This article proposes a novel, simple and elegant solution that uses some of the important features of the chest CT scan images to screen the COVID suspected patients easily and at an early phase which can be considered as an effective tool to reduce the drastic spread of this virus.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments The authors would like to express their gratitude and thank the editors, anonymous reviewers, and referees for their valuable comments and suggestions which are helpful in further improvement of this research work.

Software setup The system in which the experiments are carried out is equipped with the Microsoft Windows 7 64 bit operating system. References 1. Kim T. Learning full pairwise affinities for spectral segmentation. IEEE Trans. Pattern Anal. Chakraborty S. In: Chakraborty S. IGI Global; An overview of biomedical image analysis from the deep learning perspective. Object Recognit. Motion Detect. Video Process. A study on different edge detection techniques in digital image processing; pp.

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In: Appl. IGI GLobal; An advanced approach to detect edges of digital images for image segmentation. Expert Syst. Multi-Objective Optim. Springer Singapore; Singapore: Application of multiobjective optimization techniques in biomedical image segmentation—A study; pp.

Springer; An optimized intelligent dermatologic disease classification framework based on IoT; pp. Hore S. An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. IEEE; Contrast optimization using elitist metaheuristic optimization and gradient approximation for biomedical image enhancement; pp.

Opto-Electronics Appl. Optronix An integrated method for automated biomedical image segmentation. Mesejo P. A survey on image segmentation using metaheuristic-based deformable models: State of the art and critical analysis. Soft Comput. Chen X.

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Standards are developed in committees and only diffused if members agree on a common solution. Solutions intended as a standard can be developed by anyone. Solutions intended as a standard can come from various sources. Coordination takes place during standard development — only one solution is chosen to enter the market. Coordination takes place during diffusion — different standards are developed and compete with each other.

Windows 10 1703 download iso italy covid test – windows 10 1703 download iso italy covid test

 
Version allows wired (Ethernet) networks to be designated as metered, but Windows may still download certain updates while connected to a metered network. trol of the disease in many countries, including Italy and. Portugal,11 In a recent large survey including almost patients with COPD selected by the.

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