SimpleITK is a simplified, open source, multi-language interface to the National Library of Medicine’s Insight Segmentation and Registration Toolkit (ITK), a C++ open source image analysis toolkit which is widely used in academia and industry. SimpleITK is available in multiple programing languages including: Python, R, Java, C#, C++, Lua, Ruby, and TCL. Binary versions of the toolkit are available for the GNU Linux, Apple OS X, and Microsoft Windows operating systems. For researchers, the toolkit facilitates rapid prototyping and evaluation of image-analysis workflows with minimal effort using their programming language of choice. For educators and students, the toolkit’s concise interface and support of scripting languages facilitates experimentation with well-known algorithms, allowing them to focus on algorithmic understanding rather than low level programming skills.
The toolkit development process follows best software engineering practices including code reviews and continuous integration testing, with results displayed online allowing everyone to gauge the status of the current code and any code that is under consideration for incorporation into the toolkit. User support is available through a dedicated mailing list, the project’s Wiki, and on GitHub. The source code is freely available on GitHub under an Apache-2.0 license (github.com/SimpleITK/SimpleITK). In addition, we provide a development environment which supports collaborative research and educational activities in the Python and R programming languages using the Jupyter notebook web application. It too is freely available on GitHub under an Apache-2.0 license (github.com/InsightSoftwareConsortium/SimpleITK-Notebooks).
The first part of the presentation will describe the motivation underlying the development of SimpleITK, its development process and its current state. The second part of the presentation will be a live demonstration illustrating the capabilities of SimpleITK as a tool for reproducible research.
Dr. Ziv Yaniv is a senior computer scientist with the Office of High Performance Computing and Communications, at the National Library of Medicine, and at TAJ Technologies Inc. He obtained his Ph.D. in computer science from The Hebrew University of Jerusalem, Jerusalem Israel. Previously he was an assistant professor in the department of radiology, Georgetown university, and a principal investigator at Children’s National Hospital in Washington DC. He was chair of SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling (2013-2016) and program chair for the Information Processing in Computer Assisted Interventions (IPCAI) 2016 conference.
He believes in the curative power of open research, and has been actively involved in development of several free open source toolkits, including the Image-Guided Surgery Toolkit (IGSTK), the Insight Registration and Segmentation toolkit (ITK) and SimpleITK.
This talk will show that attitude Kalman filters can be simple in design while also being robust and accurate despite the highly nonlinear nature of attitude (i. e., orientation) estimation. Three different filters are discussed, all using quaternions and small-angle approximations of attitude errors: an Extended Kalman filter as well as an Unscented Kalman filter for a gyro-based situation, and an Extended Kalman filter for a gyro-less one. In additon to the three-axis attitude, all of the filters also estimate corrections to the angular velocity – random walk modeled biases in the gyro measured case, and first-order Markov modeled corrections in the gyro-less case, which involves angular velocity computed from mass properties and control data.
The filters are evaluated using extensive real and simulated data from low-Earth orbiting NASA satellites such as Tropical Rainfall Measurement Mission, Solar, Anomalous, and Magnetospheric Particle Explorer, Earth Radiation Budget Satellite, Wide Field Infrared Explorer, and Fast Auroral Snapshot Explorer. The evaluations predominantly involve stressing “magnetometer-only” scenarios, i. e., using only a three-axis magnetometer to sense the attitude. Comparisons are made with attitude and rate knowledge obtained using coarse sensors and single-frame algorithms, and also with results from an Unscented Kalman filter with a more complicated attitude pameterization.
Dr. Murty Challa received a B.Sc. in physics from Andhra University, Visakhapatnam, India, and a Ph.D. in physics from the University of Georgia, Athens, Georgia. His professional interests and actvities include: estimation and data fusion algorithms such as Kalman filters, batch estimators, and simultaneous localization and mapping; track correlation/ association; guidance, navigation, and control for spacecraft and unmanned vehicles; missile defense; quantum computing; statistical mechanics; computational physics; solid state physics/ materials science. He is currently a member of the Senior Professional Staff of Johns Hopkins Applied Physics Laboratory (JHU/APL), Maryland, USA. Prior to JHU/APL, he was senior staff at Institute for Defense Analyses, Alexandria, VA, and at Computer Sciences Corporation supporting NASA Goddard Space Flight Center, Greenbelt, MD. Dr. Challa’s academic positions include post-doctoral appointments in physics at Michigan State University and Virginia Commonwealth University, and an adjunct position in physics at George Washington University. He has also served as a consultant to Iridium Satellite, LLC.