A Quantitative DTI Fiber Tract Analysis Suite-898

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Kitware Kitware

http://hdl.handle.net/1926/39

A Quantitative DTI Fiber Tract Analysis Suite Presenter: Casey Goodlett Isabelle Corouge Matthieu Jomier Guido Gerig

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Acknowledgements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

DTI Fundamentals ,[object Object],[object Object],Isotropic Anisotropic Images from S. Mori

Processing Pipeline MRI Acquisition Tensor Estimation (FiberTracking) ROI Definition (InsightSNAP) Tractography (FiberTracking) Clustering (FiberViewer) Manual Editing (FiberViewer) Tract Analysis (FiberViewer) Visualization (FiberViewer)

Tensor Estimation and Tractography ,[object Object],[object Object],[object Object],[object Object],[object Object]

Clustering Fiber Tracts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Clustering Example

Fornix Clustering

Manual Editing Tools ,[object Object],[object Object],[object Object]

Visualization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Visualization ,[object Object],[object Object],[object Object],[object Object],[object Object]

Analysis of Fiber Tracts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Applications ,[object Object],[object Object]

Benefits from Open Source ,[object Object],[object Object],[object Object]

Contributions to Open Source ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Questions/Comments ,[object Object]

Contenu connexe

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Using thresholding method to segment an image, a fixed threshold is not suitable if the background is rough here, we propose a new adaptive thresholding method using KFCM. The method requires only one parameter to be selected and the adaptive threshold surface can be found automatically from the original image. An adaptive thresholding scheme using adaptive tracking and morphological filtering. KFCM algorithm computes the fuzzy membership values for each pixel. Our method is good for detecting large and small images concurrently. It is also efficient to denoise and enhance the responses of images with low local contrast can be detected. The efficiency and accuracy of the algorithm is demonstrated by the experiments on the MR brain images.

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Abstract Recently the digital imaging applications is increasing significantly and it leads the requirement of effective image compression techniques. Image compression removes the redundant information from an image. By using it we can able to store only the necessary information which helps to reduce the transmission bandwidth, transmission time and storage size of image. This paper proposed a new image compression technique using DCT-Biorthogonal Wavelet Transform with arithmetic coding for improvement the visual quality of an image. It is a simple technique for getting better compression results. In this new algorithm firstly Biorthogonal wavelet transform is applied and then 2D DCT-Biorthogonal wavelet transform is applied on each block of the low frequency sub band. Finally, split all values from each transformed block and arithmetic coding is applied for image compression. Keywords: Arithmetic coding, Biorthogonal wavelet Transform, DCT, Image Compression.

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The need to transmit or store satellite images is growing rapidly with the development of modern communications and new imaging systems. The goal of compression is to facilitate the storage and transmission of large images on the ground with high compression ratios and minimum distortion. In this work, we present a new coding scheme for satellite images. At first, the image will be downloaded followed by a fast Fourier transform FFT. The result obtained after FFT processing undergoes a scalar quantization (SQ). The results obtained after the quantization phase are encoded using entropy encoding. This approach has been tested on satellite image and Lena picture. After decompression, the images were reconstructed faithfully and memory space required for storage has been reduced by more than 80%

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Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, a generalized a novel multiple-kernel fuzzy cmeans (FCM) (NMKFCM) methodology with spatial information is introduced as a framework for image-segmentation problem. The algorithm utilizes the spatial neighborhood membership values in the standard kernels are used in the kernel FCM (KFCM) algorithm and modifies the membership weighting of each cluster. The proposed NMKFCM algorithm provides a new flexibility to utilize different pixel information in image-segmentation problem. The proposed algorithm is applied to brain MRI which degraded by Gaussian noise and Salt-Pepper noise. The proposed algorithm performs more robust to noise than other existing image segmentation algorithms from FCM family.

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IAEME Publication

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Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

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Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier. Using an analytical methodology, the proposed system blocking operation followed by sparsity to perform detection. The study outcome shows that proposed system offers an increasing level of accuracy in contrast to the existing system with faster response time.

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Understanding human mobility patterns is a significant research endeavor that has recently received considerable attention. Developing the science to describe and predict how people move from one place to another during their daily lives promises to address a wide range of societal challenges: from predicting the spread of infectious diseases, improving urban planning, to devising effective emergency response strategies. This presentation will discuss a Bayesian framework to analyse an individual’s mobility patterns and identify departures from routine. It is able to detect both spatial and temporal departures from routine based on heterogeneous sensor data (GPS, Cell Tower, social media, ..) and outperforms existing state-of-the-art predictors. Applications include mobile digital assistants (e.g., Google Now), mobile advertising (e.g., LivingSocial), and crowdsourcing physical tasks (e.g., TaskRabbit).

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This research paper proposes an improved feature reduction and classification technique to identify mild and severe dementia from brain MRI data. The manual interpretation of changes in brain volume based on visual examination by radiologist or a physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of brain MRI after identifying abnormal MRI volume, for the diagnosis of dementia. In this research work, advanced classification techniques using Support Vector Machines based on Particle Swarm Optimisation and Genetic algorithm are compared. Feature reduction by wavelets and PCA are analysed. From this analysis, it is observed that the proposed classification of SVM based PSO is found to be efficient than SVM trained with GA and wavelet based feature reduction technique yields better results than PCA.

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Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

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This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

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Remote DBA Services

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In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

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Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

A Year of the Servo Reboot: Where Are We Now?

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A Year of the Servo Reboot: Where Are We Now?

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

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AWS Community Day CPH - Three problems of Terraform

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Andrey Devyatkin

Partners Life - Insurer Innovation Award 2024

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Partners Life - Insurer Innovation Award 2024

The Digital Insurer

What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

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How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

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Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

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HTML Injection Attacks: Impact and Mitigation Strategies

HTML Injection Attacks: Impact and Mitigation Strategies

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2024: Domino Containers - The Next Step. News from the Domino Container commu...

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2024: Domino Containers - The Next Step. News from the Domino Container commu...

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Strategies for Landing an Oracle DBA Job as a Fresher

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Developing An App To Navigate The Roads of Brazil

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Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

🐬 The future of MySQL is Postgres 🐘

🐬 The future of MySQL is Postgres 🐘

🐬 The future of MySQL is Postgres 🐘

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

[2024]Digital Global Overview Report 2024 Meltwater.pdf

[2024]Digital Global Overview Report 2024 Meltwater.pdf

[2024]Digital Global Overview Report 2024 Meltwater.pdf

A Year of the Servo Reboot: Where Are We Now?

A Year of the Servo Reboot: Where Are We Now?

A Year of the Servo Reboot: Where Are We Now?

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

GenAI Risks & Security Meetup 01052024.pdf

GenAI Risks & Security Meetup 01052024.pdf

GenAI Risks & Security Meetup 01052024.pdf

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

Partners Life - Insurer Innovation Award 2024

Partners Life - Insurer Innovation Award 2024

Partners Life - Insurer Innovation Award 2024

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

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A Quantitative DTI Fiber Tract Analysis Suite-898

1. A Quantitative DTI Fiber Tract Analysis Suite Presenter: Casey Goodlett Isabelle Corouge Matthieu Jomier Guido Gerig

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6. Processing Pipeline MRI Acquisition Tensor Estimation (FiberTracking) ROI Definition (InsightSNAP) Tractography (FiberTracking) Clustering (FiberViewer) Manual Editing (FiberViewer) Tract Analysis (FiberViewer) Visualization (FiberViewer)

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11. Clustering Example

12. Fornix Clustering

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