scholarly journals MatVTK - 3D Visualization for Matlab

2009 ◽  
Author(s):  
Erich Birngruber ◽  
René Donner ◽  
Georg Langs
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Medical Image ◽  
3D Visualization ◽  
Medical Image Analysis ◽  
Medical Data ◽  
Data Sets ◽  
Steep Learning Curve ◽  
Visualization Toolkit ◽  
Crucial Part

The rapid and flexible visualization of large amounts of com- plex data has become a crucial part in medical image analysis. In re- cent years the Visualization Toolkit (VTK) has evolved as the de-facto standard for open-source medical data visualization. It features a clean design based on a data flow paradigm, which the existing wrappers for VTK (Python, Tcl/Tk, Simulink) closely follow. This allows to elegantly model many types of algorithms, but presents a steep learning curve for beginners. In contrast to existing approaches we propose a framework for accessing VTK’s capabilities from within MATLAB, using a syntax which closely follows MATLAB’s graphics primitives. While providing users with the advanced, fast 3D visualization capabilities MATLAB does not provide, it is easy to learn while being flexible enough to allow for complex plots, large amounts of data and combinations of visualiza- tions. The proposed framework will be made available as open source with detailed documentation and example data sets.

scholarly journals Medical Image Analysis with VTK: A Tutorial

2006 ◽  
Author(s):  
Xenophon Papademetris
Keyword(s):  
Image Analysis ◽  
Biomedical Engineering ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Yale University ◽  
Graduate Seminar ◽  
Draft Version ◽  
Visualization Toolkit

This paper describes a new tutorial book titled “An Introduction to Programming for Medical Image Analysis with the Visualization Toolkit.” This book derived from a set of class handouts used in a biomedical engineering graduate seminar at Yale University. The goal for the seminar was to introduce the students to the Visualization Toolkit (VTK) and, to a lesser extent, the Insight Toolkit (ITK). A draft version of the complete book (including all the sample code) is available online at www.bioimagesuite.org/vtkbook.

scholarly journals Cerebral Cortical Thickness Estimation using the TINA Open-Source Image Analysis Environment

2005 ◽  
Author(s):  
Marietta Scott ◽  
Paul A. Bromiley ◽  
Neil A. Thacker
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Cortical Thickness ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Magnetic Resonance Images ◽  
Source Image ◽  
Thickness Estimation ◽  
Analysis Environment

This paper gives an overview of the use and development of the TINA open-source medical image analysis environment, with respect to the determination of human cerebral cortical thickness estimation from magnetic resonance images. The ultimate aim of TINA is to provide a validated system where the source code and datasets are freely available in order to allow peer-validation of published results.

scholarly journals BioImage Suite: An integrated medical image analysis suite: An update

2006 ◽  
Author(s):  
Xenophon Papademetris ◽  
Marcel Jackowski ◽  
Nallkkandi Rajeevan ◽  
Marcello DiStasio ◽  
Hirohito Okuda ◽  
...  
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Diffusion Weighted Image ◽  
Analysis Software ◽  
Visualization Toolkit ◽  
Scripting Language ◽  
Fmri Analysis ◽  
User Friendly ◽  
Beta Version

BioImage Suite is an NIH-supported medical image analysis software suite developed at Yale. It leverages both the Visualization Toolkit (VTK) and the Insight Toolkit (ITK) and it includes many additional algorithms for image analysis especially in the areas of segmentation, registration, diffusion weighted image processing and fMRI analysis. BioImage Suite has a user-friendly user interface developed in the Tcl scripting language. A final beta version is freely available for download.

scholarly journals The MITK Approach

2005 ◽  
Author(s):  
Ivo Wolf ◽  
Marco Nolden ◽  
Thomas Boettger ◽  
Ingmar Wegner ◽  
Max Schoebinger ◽  
...  
Keyword(s):  
Image Analysis ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Interactive Systems ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Visualization Toolkit ◽  
Key Features ◽  
2D And 3D

The Medical Imaging Interaction Toolkit (MITK) is an opensource toolkit for the development of interactive medical image analysis software. MITK is based on the open-source Insight Toolkit (ITK) and Visualization Toolkit (VTK) and extends them with features required for interactive systems. ITK is used for the algorithmic scope and general infrastructure, VTK for visualization. Key features of MITK are the coordination of multiple 2D and 3D visualizations of arbitrary data, a general interaction concept including undo/redo, and its extendibility and flexibility to create tailored applications due to its toolkit character and different layers of hidden complexity. The paper gives a brief introduction into the overall concepts and goals of the MITK approach. Suggestions and participation are welcome. MITK is available at www.mitk.org.

scholarly journals MIA - A free and open source software for gray scale medical image analysis

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Gert Wollny ◽  
Peter Kellman ◽  
María-Jesus Ledesma-Carbayo ◽  
Matthew M Skinner ◽  
Jean-Jaques Hublin ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Gray Scale

scholarly journals ITK Tutorial Presentation Slides

2006 ◽  
Author(s):  
Luis Ibanez ◽  
Lydia Ng ◽  
Josh Cates ◽  
Stephen Aylward ◽  
Bill Lorensen ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Data Representation ◽  
Medical Image Segmentation ◽  
Object Oriented Programming ◽  
Application Development

This course introduces attendees to select open-source efforts in the field of medical image analysis. Opportunities for users and developers are presented. The course particularly focuses on the open-source Insight Toolkit (ITK) for medical image segmentation and registration. The course describes the procedure for downloading and installing the toolkit and covers the use of its data representation and filtering classes. Attendees are shown how ITK can be used in their research, rapid prototyping, and application development.LEARNING OUTCOMES After completing this course, attendees will be able to: contribute to and benefit from open-source software for medical image analysis download and install the ITK toolkit start their own software project based on ITK design and construct an image processing pipeline combine ITK filters for medical image segmentation combine ITK components for medical image registrationINTENDED AUDIENCE This course is intended for anyone involved in medical image analysis. In particular it targets graduate students, researchers and professionals in the areas of computer science and medicine. Attendees should have an intermediate level on object oriented programming with C++ and must be familiar with the basics of medical image processing and analysis.

scholarly journals BioImage Suite: An integrated medical image analysis suite

2005 ◽  
Author(s):  
Xenophon Papademetris ◽  
Marcel Jackowski ◽  
Nallakkandi Rajeevan ◽  
R. Todd Constable ◽  
Lawrence Staib
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Analysis Software ◽  
Software Suite ◽  
Abdominal Image ◽  
Image Analysis Software ◽  
Visualization Toolkit

BioImage Suite is an integrated image analysis software suite developed at Yale. It uses a combination of C++ and Tcl in the same fashion as that pioneered by the Visualization Toolkit (VTK) and it leverages both VTK and the Insight Toolkit. It has extensive capabilities for both neuro/cardiac and abdominal image analysis and state of the art visualization. It is currently in use at Yale; a first public release is expected before the end of 2005.

scholarly journals Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

2022 ◽  
Vol 12 (2) ◽  
pp. 681
Author(s):  
JiHwan Lee ◽  
Seok Won Chung
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Data ◽  
Future Research ◽  
Profound Impact ◽  
Orthopedic Surgeons ◽  
Insight Into

Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

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