DOCSLIB.ORG

Getting Started First Steps with Mevislab

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Getting Started First Steps with Mevislab Getting Started First Steps with MeVisLab 1 Getting Started Getting Started Copyright © 2003-2021 MeVis Medical Solutions Published 2021-05-11 2 Table of Contents 1. Before We Start ................................................................................................................... 10 1.1. Welcome to MeVisLab ............................................................................................... 10 1.2. Coverage of the Document ........................................................................................ 10 1.3. Intended Audience ..................................................................................................... 11 1.4. Conventions Used in This Document .......................................................................... 11 1.4.1. Activities ......................................................................................................... 11 1.4.2. Formatting ...................................................................................................... 11 1.5. How to Read This Document ..................................................................................... 12 1.6. Related MeVisLab Documents ................................................................................... 12 1.7. Glossary (abbreviated) ............................................................................................... 13 2. The Nuts and Bolts of MeVisLab ........................................................................................... 15 2.1. MeVisLab Basics ....................................................................................................... 15 2.2. Development in MeVisLab ......................................................................................... 16 2.3. MeVisLab Modules .................................................................................................... 17 2.4. Fields ........................................................................................................................ 18 2.5. Networks ................................................................................................................... 19 2.6. Overview of Important Files ........................................................................................ 20 2.7. User Interfaces Controls ............................................................................................ 21 2.8. Scripting .................................................................................................................... 22 2.9. How to Find More Information on Networks and Modules ............................................. 22 3. Loading and Viewing Images ................................................................................................ 24 3.1. The MeVisLab GUI .................................................................................................... 24 3.2. Searching and Adding Modules .................................................................................. 25 3.3. Using the ImageLoad Module ..................................................................................... 28 3.4. Adding Viewers to ImageLoad .................................................................................... 33 3.4.1. Adding the View2D Module ............................................................................. 33 3.4.2. Adding the View3D Module ............................................................................. 37 3.5. Alternative Ways to Load Images ............................................................................... 37 3.5.1. Dragging Images onto the Workspace .............................................................. 37 3.5.2. Using the LocalImage Module ......................................................................... 38 3.6. A Note on Importing DICOM Images .......................................................................... 39 4. Implementing a Contour Filter ............................................................................................... 41 4.1. Loading the Input Image ............................................................................................ 41 4.2. Implementing the Contour Filter .................................................................................. 42 4.3. Parameter Connection for Synchronization .................................................................. 46 5. Defining a Region of Interest (ROI) ....................................................................................... 49 5.1. Creating a Viewer with a Selection Rectangle ............................................................. 50 5.2. Adding a Second Viewer for the Subimage ................................................................. 50 5.3. Adding the Interactivity for the Viewers ....................................................................... 51 6. Excursion: Functionality Overview ......................................................................................... 56 6.1. Image Handling and Processing ................................................................................. 56 6.1.1. Image Handling .............................................................................................. 56 6.1.2. Image Properties ............................................................................................ 56 6.1.3. Basic Image Processing .................................................................................. 56 6.1.4. Filter .............................................................................................................. 57 6.1.5. Segmentation ................................................................................................. 57 6.2. Visualization .............................................................................................................. 57 6.2.1. 2D Viewing ..................................................................................................... 57 6.2.2. 3D Viewing ..................................................................................................... 58 6.2.3. Lookup Tables ................................................................................................ 58 6.3. Data Objects ............................................................................................................. 58 6.3.1. Markers .......................................................................................................... 58 6.3.2. Curves ........................................................................................................... 59 6.3.3. Contours ........................................................................................................ 59 6.3.4. Surface objects ............................................................................................... 59 3 Getting Started 6.4. Miscellaneous ............................................................................................................ 59 6.4.1. Fields ............................................................................................................. 59 6.4.2. Diagnostic ...................................................................................................... 60 7. Creating an Open Inventor Scene ......................................................................................... 61 7.1. Introduction to Open Inventor ..................................................................................... 62 7.2. Creating the Applicator .............................................................................................. 64 7.3. Creating the Interaction .............................................................................................. 67 7.4. Creating the Anatomical Image .................................................................................. 70 7.5. Finishing the Complete Open Inventor Scene .............................................................. 71 8. Starting Development with Package Creation ......................................................................... 75 8.1. What are Packages ................................................................................................... 75 8.2. Creating a User Package for Your Project ................................................................... 77 9. Introduction to Macro Modules .............................................................................................. 78 10. Developing a Macro Module for an Applicator ...................................................................... 80 10.1. Creating a Basic Global Macro ................................................................................. 80 10.2. Adding the Macro Parameters and Panel .................................................................. 84 10.3. Programming the Python Script ................................................................................ 89 10.4. Addition: Shifting the Whole Tip ................................................................................ 94 11. GUI Design in MeVisLab .................................................................................................... 97 11.1. MeVisLab Definition Language (MDL) ....................................................................... 98 11.1.1. MDL Validator ............................................................................................... 98 11.1.2. MDL Controls ..............................................................................................
Load more

Recommended publications
  • Inviwo — a Visualization System with Usage Abstraction Levels
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL X, NO. Y, MAY 2019 1 Inviwo — A Visualization System with Usage Abstraction Levels Daniel Jonsson,¨ Peter Steneteg, Erik Sunden,´ Rickard Englund, Sathish Kottravel, Martin Falk, Member, IEEE, Anders Ynnerman, Ingrid Hotz, and Timo Ropinski Member, IEEE, Abstract—The complexity of today’s visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities. Index Terms—Visualization systems, data visualization, visual analytics, data analysis, computer graphics, image processing. F 1 INTRODUCTION The field of visualization is maturing, and a shift can be employing different layers of abstraction.
    [Show full text]
  • A Client/Server Based Online Environment for the Calculation of Medical Segmentation Scores
    A Client/Server based Online Environment for the Calculation of Medical Segmentation Scores Maximilian Weber, Daniel Wild, Jürgen Wallner, Jan Egger Abstract— Image segmentation plays a major role in medical for medical image segmentation are often very large due to the imaging. Especially in radiology, the detection and development broadly ranged possibilities offered for all areas of medical of tumors and other diseases can be supported by image image processing. Often huge updates are needed, which segmentation applications. Tools that provide image require nightly builds, even if only the functionality of segmentation and calculation of segmentation scores are not segmentation is needed by the user. Examples are 3D Slicer available at any time for every device due to the size and scope [8], MeVisLab [9] and MITK [10], [11], which have installers of functionalities they offer. These tools need huge periodic that are already over 1 GB. In addition, in sensitive updates and do not properly work on old or weak systems. environments, like hospitals, it is mostly not allowed at all to However, medical use-cases often require fast and accurate install these kind of open-source software tools, because of results. A complex and slow software can lead to additional security issues and the potential risk that patient records and stress and thus unnecessary errors. The aim of this contribution data is leaked to the outside. is the development of a cross-platform tool for medical segmentation use-cases. The goal is a device-independent and The second is the need of software for easy and stable always available possibility for medical imaging including manual segmentation training.
    [Show full text]
  • Do Mlabs Still Make a Difference? a Second Assessment Public Disclosure Authorized Public Disclosure Authorized
    Public Disclosure Authorized Do mLabs Still Make a Difference? A Second Assessment Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized infoDv INNOVATION & ENTREPRENEURSHIP Do mLabs Still Make a b Difference? A Second Assessment © 2017 The World Bank Group 1818 H Street NW Washington, DC 20433 Website: www.infodev.org Email: [email protected] Twitter: @infoDev Facebook: /infoDevWBG For more resources and information about mLabs, visit: https://www.infodev.org/report/do-mlabs-still-make- difference-second-assessment This work is a product of the staff of infoDev/World Bank Group. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the donors of infoDev, the World Bank Group, its Board of Directors, or the governments they represent. The World Bank Group does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of the World Bank Group concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions: This work is available under the Creative Commons Attribution 3.0 Unported license (CC BY 3.0) http://creativecommons.org/licenses/ by/3.0. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following conditions: Attribution: Please cite the work as follows: “Do mLabs Still Make a Difference? A Second Assessment” 2017. Washington, DC: The World Bank Group. License: Creative Commons Attribution CC BY 3.0 Photo Credits: Cover Photo: Shutterstock 2017 c ACKNOWLEDGEMENTS This assessment would not have been possible This assessment was produced by Sonjara, without the valuable contribution of all mLab Inc.
    [Show full text]
  • Introduction to MLAB the Royal Road to Dynamical Simulations
    Introduction to MLAB The Royal Road to Dynamical Simulations Revision Date: September 24, 2015 Foster Morrison, author Turtle Hollow Associates, Inc. P.O. Box 3639 Gaithersburg, MD 20885 USA Daniel Kerner, editor Civilized Software, Inc. 12109 Heritage Park Circle Silver Spring, MD 20906 USA Email: [email protected] Web-site: http://www.civilized.com (301) 962-3711 c 1992-2014 Turtle Hollow Associates, Inc. Contents Preface ii 1 BASIC MATHEMATICS AND ITS IMPLEMENTATION IN MLAB 1 1.1 What You Need to Know ................................ 1 1.2 Getting Started with MLAB ............................... 2 1.3 Foundations of Mathematics ............................... 2 1.4 Sets ............................................ 3 1.5 Numbers, Prime, Rational, and Real .......................... 4 1.6 Try Out MLAB ...................................... 6 2 PROGRAMMING IN MLAB 10 2.1 More Numerical Analysis vs. Analysis ......................... 10 2.2 Legendre Polynomials .................................. 11 3 VECTORS AND MATRICES 13 3.1 Vectors .......................................... 13 3.2 Matrices, Linear Algebra, and Modern Algebra .................... 14 3.3 Arrays ........................................... 16 3.4 Functional Analysis .................................... 16 i 4 LINEAR DYNAMIC SYSTEMS AND MODELS 19 4.1 Difference Equations ................................... 19 4.2 Differential Equations .................................. 21 4.3 Generalizations ...................................... 23 5 LINEAR DYNAMIC SYSTEMS WITH INPUTS
    [Show full text]
  • Medical Image Processing Software
    Wohlers Report 2018 Medical Image Processing Software Medical image Patient-specific medical devices and anatomical models are almost always produced using radiological imaging data. Medical image processing processing software is used to translate between radiology file formats and various software AM file formats. Theoretically, any volumetric radiological imaging dataset by Andy Christensen could be used to create these devices and models. However, without high- and Nicole Wake quality medical image data, the output from AM can be less than ideal. In this field, the old adage of “garbage in, garbage out” definitely applies. Due to the relative ease of image post-processing, computed tomography (CT) is the usual method for imaging bone structures and contrast- enhanced vasculature. In the dental field and for oral- and maxillofacial surgery, in-office cone-beam computed tomography (CBCT) has become popular. Another popular imaging technique that can be used to create anatomical models is magnetic resonance imaging (MRI). MRI is less useful for bone imaging, but its excellent soft tissue contrast makes it useful for soft tissue structures, solid organs, and cancerous lesions. Computed tomography: CT uses many X-ray projections through a subject to computationally reconstruct a cross-sectional image. As with traditional 2D X-ray imaging, a narrow X-ray beam is directed to pass through the subject and project onto an opposing detector. To create a cross-sectional image, the X-ray source and detector rotate around a stationary subject and acquire images at a number of angles. An image of the cross-section is then computed from these projections in a post-processing step.
    [Show full text]
  • Sr. Software Engineer @ Mlab
    Sr. Software Engineer @ mLab About mLab: mLab is a fully managed cloud database service featuring highly available MongoDB databases, automated backups, web- based tools, 24/7 monitoring, and expert support. Developers absolutely love us because our Database-as-a-Service (DBaaS) allows them to focus their attention on product development instead of operations. mLab is one of the fastest-growing companies in the cloud infrastructure space. We're headquartered in the Mission/Potrero area of San Francisco and are well-funded by premier venture and angel investors including Foundry Group, Baseline Ventures, Upfront Ventures, Freestyle Capital and David Cohen of TechStars. What makes us happiest? Innovating, automating, helping other software developers, and giving back to our community. In addition to checking out our website at http://mlab.com and our blog at http://blog.mlab.com. Who we’re looking for: We are looking for a Sr. Software Engineer who will join us in scaling mLab from managing >100,000 databases to managing millions of databases at ever-increasing levels of complexity. In the process, you’ll be exposed to every major cloud provider and infrastructure technology. We currently run on AWS, Google Cloud Platform, and Windows Azure and have integrated with all of the major Platform-as-a-Service providers (Heroku et al.). You'll become an expert in MongoDB, cloud automation, and mLab’s suite of automation tools (some of which we open-source). You’ll work on things like: n Node.js and Python tools/services for automating the entire
    [Show full text]
  • Migrating from Mongodb to Mongodb Atlas on the AWS Cloud AWS Prescriptive Guidance Migrating from Mongodb to Mongodb Atlas on the AWS Cloud
    AWS Prescriptive Guidance Migrating from MongoDB to MongoDB Atlas on the AWS Cloud AWS Prescriptive Guidance Migrating from MongoDB to MongoDB Atlas on the AWS Cloud AWS Prescriptive Guidance: Migrating from MongoDB to MongoDB Atlas on the AWS Cloud Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. AWS Prescriptive Guidance Migrating from MongoDB to MongoDB Atlas on the AWS Cloud Table of Contents Introduction ...................................................................................................................................... 1 Overview ........................................................................................................................................... 2 MongoDB migration at a glance ................................................................................................... 2 Roles and responsibilities ............................................................................................................ 3 Cost and licensing ...................................................................................................................... 3 MongoDB and MongoDB Atlas
    [Show full text]
  • Developer Advocate @ Mlab Aka Crafty Coder Who Loves Evangelism
    Developer Advocate @ mLab aka Crafty Coder Who Loves Evangelism About mLab: mLab is one of the fastest growing companies in the cloud infrastructure space. The company is solving mission-critical challenges faced by developers who require innovative database technology to support their applications. Our solution is built on MongoDB, the leading NoSQL database which is disrupting the multi-billion dollar database market. We're headquartered in the Mission/ Potrero area of San Francisco and are well-funded by premier venture and angel investors including Foundry Group, Baseline Ventures, Upfront Ventures, Freestyle Capital, and David Cohen of TechStars. Our users love our Database-as-a-Service (DBaaS) solution, as it allows them to focus their attention on product development, instead of operations. Developers create thousands of new databases per month using our fully managed cloud database service which offers highly available MongoDB databases on the most popular cloud providers. Our customers love our top-tier support, automated backups, web-based management, performance enhancement tools, and 24/7 monitoring. Looking forward, our roadmap includes a suite of new capabilities which will have a massive impact on the efficiency with which developers write and deploy applications. We’re biased (of course), but we believe our culture is one of our greatest assets. What makes us happiest? Innovating, automating, helping software developers, and giving back to our community. To get a better taste for who we are, visit our website at http://mlab.com and read our blog at http://blog.mlab.com. The role: We are looking for a code-slinger with a (latent?) passion for marketing and evangelism who will join us in recruiting and driving the success of current and future mLab users.
    [Show full text]
  • Getting Started First Steps with Mevislab
    Getting Started First Steps with MeVisLab 1 Getting Started Getting Started Published April 2009 Copyright © MeVis Medical Solutions, 2003-2009 2 Table of Contents 1. Before We Start ..................................................................................................................... 1 1.1. Welcome to MeVisLab ................................................................................................. 1 1.2. Coverage of the Document .......................................................................................... 1 1.3. Intended Audience ....................................................................................................... 1 1.4. Requirements .............................................................................................................. 2 1.5. Conventions Used in This Document ............................................................................ 2 1.5.1. Activities ........................................................................................................... 2 1.5.2. Formatting ........................................................................................................ 2 1.6. How to Read This Document ....................................................................................... 2 1.7. Related MeVisLab Documents ..................................................................................... 3 1.8. Glossary (abbreviated) ................................................................................................. 4 2. The Nuts
    [Show full text]
  • Odin: Microsoft's Scalable Fault-Tolerant CDN Measurement
    Odin: Microsoft’s Scalable Fault-Tolerant CDN Measurement System Matt Calder, Microsoft/USC; Manuel Schröder, Ryan Gao, Ryan Stewart, and Jitendra Padhye, Microsoft; Ratul Mahajan, Intentionet; Ganesh Ananthanarayanan, Microsoft; Ethan Katz-Bassett, Columbia University https://www.usenix.org/conference/nsdi18/presentation/calder This paper is included in the Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’18). April 9–11, 2018 • Renton, WA, USA ISBN 978-1-931971-43-0 Open access to the Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation is sponsored by USENIX. Odin: Microsoft’s Scalable Fault-Tolerant CDN Measurement System Matt Calder,†? Manuel Schroder,¨ † Ryan Gao,† Ryan Stewart,† Jitendra Padhye† Ratul Mahajan,# Ganesh Ananthanarayanan,† Ethan Katz-Bassett§ Microsoft† USC? Intentionet# Columbia University§ Abstract Microsoft operates a CDN with over 100 PoPs around the world to host applications critical to Microsoft’s busi- Content delivery networks (CDNs) are critical for deliv- ness such as Office, Skype, Bing, Xbox, and Windows ering high performance Internet services. Using world- Update. This work presents our experience designing a wide deployments of front-ends, CDNs can direct users system to meet the measurement needs of Microsoft’s to the front-end that provides them with the best latency global CDN. We first describe the key requirements and availability. The key challenges arise from the het- needed to support Microsoft’s CDN operations. Existing erogeneous connectivity of clients and the dynamic na- approaches to collecting measurements were unsuitable ture of the Internet that influences latency and availabil- for at least one of two reasons: ity.
    [Show full text]
  • An Open-Source Visualization System
    OpenWalnut { An Open-Source Visualization System Sebastian Eichelbaum1, Mario Hlawitschka2, Alexander Wiebel3, and Gerik Scheuermann1 1Abteilung f¨urBild- und Signalverarbeitung, Institut f¨urInformatik, Universit¨atLeipzig, Germany 2Institute for Data Analysis and Visualization (IDAV), and Department of Biomedical Imaging, University of California, Davis, USA 3 Max-Planck-Institut f¨urKognitions- und Neurowissenschaften, Leipzig, Germany [email protected] Abstract In the last years a variety of open-source software packages focusing on vi- sualization of human brain data have evolved. Many of them are designed to be used in a pure academic environment and are optimized for certain tasks or special data. The open source visualization system we introduce here is called OpenWalnut. It is designed and developed to be used by neuroscientists during their research, which enforces the framework to be designed to be very fast and responsive on the one side, but easily extend- able on the other side. OpenWalnut is a very application-driven tool and the software is tuned to ease its use. Whereas we introduce OpenWalnut from a user's point of view, we will focus on its architecture and strengths for visualization researchers in an academic environment. 1 Introduction The ongoing research into neurological diseases and the function and anatomy of the brain, employs a large variety of examination techniques. The different techniques aim at findings for different research questions or different viewpoints of a single task. The following are only a few of the very common measurement modalities and parts of their application area: computed tomography (CT, for anatomical information using X-ray measurements), magnetic-resonance imag- ing (MRI, for anatomical information using magnetic resonance esp.
    [Show full text]
  • An Efficient Support for Visual Computing in Surgical Planning and Training
    Nr.: FIN-004-2008 The Medical Exploration Toolkit - An efficient support for visual computing in surgical planning and training Konrad Mühler, Christian Tietjen, Felix Ritter, Bernhard Preim Arbeitsgruppe Visualisierung (ISG) Fakultät für Informatik Otto-von-Guericke-Universität Magdeburg Impressum (§ 10 MDStV): Herausgeber: Otto-von-Guericke-Universität Magdeburg Fakultät für Informatik Der Dekan Verantwortlich für diese Ausgabe: Otto-von-Guericke-Universität Magdeburg Fakultät für Informatik Konrad Mühler Postfach 4120 39016 Magdeburg E-Mail: [email protected] http://www.cs.uni-magdeburg.de/Preprints.html Auflage: 50 Redaktionsschluss: Juni 2008 Herstellung: Dezernat Allgemeine Angelegenheiten, Sachgebiet Reproduktion Bezug: Universitätsbibliothek/Hochschulschriften- und Tauschstelle The Medical Exploration Toolkit – An efficient support for visual computing in surgical planning and training Konrad M¨uhler, Christian Tietjen, Felix Ritter, and Bernhard Preim Abstract—Application development is often guided by the usage of software libraries and toolkits. For medical applications, the currently available toolkits focus on image analysis and volume rendering. Advanced interactive visualizations and user interface issues are not adequately supported. Hence, we present a toolkit for application development in the field of medical intervention planning, training and presentation – the MEDICALEXPLORATIONTOOLKIT (METK). The METK is based on rapid prototyping platform MeVisLab and offers a large variety of facilities for an easy and efficient application development process. We present dedicated techniques for advanced medical visualizations, exploration, standardized documentation and interface widgets for common tasks. These include, e.g., advanced animation facilities, viewpoint selection, several illustrative rendering techniques, and new techniques for object selection in 3d surface models. No extended programming skills are needed for application building, since a graphical programming approach can be used.
    [Show full text]