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The proposed method can handle only a single objective at a time. Therefore, the proposed approach is not suitable for multi-objective optimization issues unless enhanced further. The number of images in the dataset is not very large. So, the proposed approach can also be tested on some additional CT images of COVID infection as well as on some standard dataset of the biomedical images. It neither use any training dataset nor uses any pre-trained model. The proposed approach can effectively segment the radiological images that are collected from different patients i.
It is to be clarified that this approach cannot take any decision about the type of disease automatically. For example, the proposed approach cannot automatically differentiate between COVID related lung images and other lung diseases. This approach aims to help physicians in early and quick interpretation of the radiological images and diagnosis of the diseases without any manual delineations.
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. From Fig. Both qualitative and quantitative study produces some satisfactory results which help to make the proposed approach trustworthy so that it can be reliably adapted in the real-world scenarios.
The proposed approach initially performs a superpixel-based clustering using the proposed superpixel computation method which significantly reduces the computational overhead for the further clustering process by reducing a large amount of spatial information. Therefore, radiological images can be conveniently explicated with the application of the proposed method and the proposed approach is also helpful in the easy interpretation of the radiological images.
The proposed work neither claims that the suggested approach is cent percent accurate in determining the COVID infection nor claims that it can be a replacement of the RT-PCR test but, the proposed method can help detect some common characteristics from the CT scan images, that can help to isolate some suspected patients from the rest of the community.
The proposed approach is helpful for the early screening of the COVID besides being a significant contribution to the image segmentation literature. 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.
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. The dedicated graphics memory is not utilized for any kind of processing purposes. The system in which the experiments are carried out is equipped with the Microsoft Windows 7 64 bit operating system.
It is not at all essential to use the Matlab environment to implement the proposed approach. We have chosen Matlab due to the availability of some inbuilt functions which are helpful to reduce the coding complexity. Still, any other languages or platforms can be used to implement the same. It is assumed that there are no manual annotations available and the proposed approach is capable to process the images without having any prior knowledge.
The final segmented images are constructed by assigning the superpixel to their corresponding cluster centers. These segmented images are helpful to interpret different features from these radiological images. 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.
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Notes Bioinformatics Springer Verlag; Liver segmentation from CT scans: A survey; pp. Litjens G. A survey on deep learning in medical image analysis. Image Anal. Jentzen W. The upgrade failed, and her computer was left in a broken state thereafter, which disrupted the ability to run her travel agency.
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In March , Microsoft announced that it would display notifications informing users on Windows 7 devices of the upcoming end of extended support for the platform, and direct users to a website urging them to upgrade to Windows 10 or purchase new hardware.
This dialog will be similar to the previous Windows 10 upgrade prompts, but will not explicitly mention Windows Privacy advocates and other critics have expressed concern regarding Windows 10’s privacy policies and its collection and use of customer data. Users can opt out from most of this data collection, [] [] but telemetry data for error reporting and usage is also sent to Microsoft, and this cannot be disabled on non-Enterprise editions of Windows Rock Paper Shotgun writer Alec Meer argued that Microsoft’s intent for this data collection lacked transparency, stating that “there is no world in which 45 pages of policy documents and opt-out settings split across 13 different settings screens and an external website constitutes ‘real transparency’.
The Russian government had passed a federal law requiring all online services to store the data of Russian users on servers within the country by September or be blocked. But Microsoft is held to a different standard than other companies”. The Microsoft Services agreement reads that the company’s online services may automatically “download software updates or configuration changes, including those that prevent you from accessing the Services, playing counterfeit games, or using unauthorized hardware peripheral devices.
In September , Microsoft hid the option to create a local account during a fresh installation if a PC is connected to the internet. This move was criticized by users who did not want to use an online Microsoft account.
In late-July , Windows Defender began to classify modifications of the hosts file that block Microsoft telemetry servers as being a severe security risk. From Wikipedia, the free encyclopedia. Redirected from Windows 10 Fall Creators Update. This is the latest accepted revision , reviewed on 20 December This article is about the operating system for personal computers. For the related now discontinued operating system for mobile devices, see Windows 10 Mobile.
For the series of operating systems produced from to , see Windows 9x. Closed-source source-available through the Shared Source Initiative Some components free and open-source [1] [2] [3] [4]. List of languages. For the Windows versions produced from to , see Windows 9x. For the Windows version following Windows 8, see Windows 8. Main article: Features new to Windows See also: List of features removed in Windows Main article: List of typefaces included with Microsoft Windows.
Main article: Windows 10 editions. See also: Windows Insider. Main article: Windows 10 version history. Main article: Criticism of Windows This section duplicates the scope of other articles , specifically Criticism of Windows Please discuss this issue on the talk page and edit it to conform with Wikipedia’s Manual of Style by replacing the section with a link and a summary of the repeated material or by spinning off the repeated text into an article in its own right.
June Windows PC market share of Windows statistics Windows Business and economics portal. Retrieved August 31, Microsoft Support. Windows Insider Blog. November 10, Retrieved June 13, NET Core 3.
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PC Pro. July 29, April 23, Retrieved July 16, March 20, Microsoft says Hello to palm-vein biometrics”. Retrieved February 10, March 17, Retrieved March 17, Retrieved July 18, Microsoft Docs. Retrieved October 30, Windows Developer Blog. June 17, Retrieved January 2, This means you can now use WSL for machine learning, artificial intelligence, and data science scenarios more easily when big data sets are involved.
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FLAC files all on its own”. PC Games Hardware in German. May 5, Retrieved April 11, DirectX Developer Blog. Archived from the original on October 4, Retrieved October 3, October 3, March 21, Retrieved June 20, PC Perspective. Archived from the original on September 5, Retrieved August 22, May 13, Retrieved May 13, Retrieved February 3, May 2, February 3, Retrieved March 11, April 21, Retrieved April 23, Retrieved March 8, Retrieved October 1, October June 21, Retrieved June 22, Conde Nast Digital.
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Archived from the original on August 1, March 18, May 19, Retrieved May 21, Retrieved July 14, Retrieved May 18, Windows Help. Archived from the original on May 1, Retrieved February 1, May 16, While our free offer to upgrade to Windows 10 will not apply to Non-Genuine Windows devices. March 19, Retrieved March 19, Retrieved August 13, July 31, Applications software consists of user-focused programs that include web browsers, database programs, etc.
Therefore, Software engineers apply the principles of software engineering to the design, development, maintenance, testing, and evaluation of computer software. They have extensive knowledge of programming languages, software development, and computer operating systems, and they apply engineering principles to software creation.
By applying these engineering principles to every stage of the development process, from requirements analysis to the software process, they can create customized systems for individual clients.
T his is a field of engineering that focuses on cloud services, such as “software as a service”, “platform as a service”, and “infrastructure as a service”.
It is a multidisciplinary method encompassing contributions from diverse areas such as systems engineering , software engineering , web engineering , performance engineering , information technology engineering , security engineering , platform engineering, service engineering, risk engineering, and quality engineering.
Cloud network engineers specialize in migrating functions and networks to private, public, and hybrid cloud infrastructures. In doing so, they give organizations the freedom and flexibility to maximize their flexibility and productivity. Therefore, These tech professionals need to be experts in networking and cloud hardware and software, as they are responsible for the execution, design, upkeep and day-to-day support of network and cloud services.
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