Ms. Melissa Trezise Profile

Melissa Trezise

at Queen's University

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Proceedings Article | 12 March 2014 Paper

SimITK: model driven engineering for medical imaging

Melissa Trezise, David Gobbi, James Cordy, Purang Abolmaesumi, Parvin Mousavi

Proceedings Volume 9036, 903622 (2014) https://doi.org/10.1117/12.2043197

KEYWORDS: Simulink, Image registration, Medical imaging, Process modeling, Image filtering, MATLAB, Computer programming, Image segmentation, Algorithm development, Image processing

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The Insight Segmentation and Registration Toolkit (ITK) is a highly utilized open source medical imaging library providing chiefly the functionality to register, segment, and filter medical images. Although extremely powerful, ITK has a steep learning curve for users with little or no background in programming. It was for this reason that SimITK was developed. SimITK wraps ITK into the model driven engineering environment Simulink, a part of the Matlab development suite. The first released version of SimITK was a proof of concept, and demonstrated that ITK could be wrapped successfully in Simulink. In this paper a new version of SimITK is presented where ITK classes are wrapped using a fully automated process. In addition, SimITK is transitioned to successfully support ITK version 4, in order to remain current with the ITK project. SimITK includes thirty-seven image filters, twelve optimizers, and nineteen transform classes from ITK version 4 which are successfully wrapped and tested, and can be quickly and easily combined to perform medical imaging tasks. These classes were chosen to represent a broad range of usability, and to allow for greater flexibility when creating registration pipelines. SimITK has the potential to reduce the learning curve for ITK and allow the user to focus on developing workflows and algorithms. A release of SimITK along with tutorials and videos is available at www.simitkvtk.com.

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