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 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 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 Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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.

Tutorial: Deep Learning Advancing the State-of-the-Art in Medical Image Analysis

2017 ◽  
pp. 6-7 ◽  
Author(s):  
Vincent Christlein ◽  
Florin C. Ghesu ◽  
Tobias Würfl ◽  
Andreas Maier ◽  
Fabian Isensee ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
The State

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.

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