Course details of CS 736 - Algorithms for Medical Image Processing

Course Name Algorithms for Medical Image Processing
Total Credits 6
Type T
Lecture 3
Tutorial 0
Practical 0
Selfstudy 0
Half Semester N
Prerequisite
Text Reference 1. Guide to Medical Image Analysis: Methods and Algorithms Author: Klaus D. Toennies Springer, 2012. ISBN 978-1-4471-2751-2 2. Mathematics of Medical Imaging Author: Charles L. Epstein Prentice Hall, 2003. ISBN 9780130675484 3. Medical Image Reconstruction: A Conceptual Tutorial Author: Gengsheng L. ZengSpringer, 2010. ISBN 978-3-642-05368-9 4. Statistical Models of Shape: Optimisation and Evaluation Authors: Rhodri H. Davies, Carole J. Twining, Chris J. Taylor Springer, 2010. ISBN 978-1-84800-137-4 5. Medical Image Registration Authors: Joseph V. Hajnal, Derek L.G. Hill, David Hawkes CRC Press, 2001. ISBN: 0849300649 Reference Notes 1. Biomedical Signal and Image Processing, Spring 2007 MIT Open Course Ware: Massachusetts Institute of Technology MIT Course Number: HST.582J Authors: Gari Clifford, John Fisher, Julie Greenberg, William Wells http://ocw.mit.edu/courses/health-sciences-andtechnology/ hst-582j-biomedical-signal-andimage- processing-spring-2007
Description The topics covered will include the following: 1. Introduction to imaging modalities, mathematical imaging models, noise and artefact models, sampling, signal modelling and fitting X ray, computed tomography (CT), positron 302225 emission tomography (PET), magnetic resonance imaging (MRI) (including diffusion MRI, functional MRI), ultrasound, microscopy 2. Visualization Methods: sectioning, multimodal images, 302225 overlays, rendering surfaces and volumes, using glyphs Application domains: 3D imaging, PET-CT 302225 imaging, diffusion tensor imaging 3. Image reconstruction Methods: image models, sampling, problem 302225 formulations, algorithms Application domains: MRI, CT 302225 4. Image denoising Methods: Bayesian estimation, nonlinear 302225 smoothing Application domains: MRI, CT, others 302225 5. Image segmentation Methods: clustering, Bayesian estimation, 302225 graph partitioning, classification Application domains: brain, heart, knee, 302225 thorax, abdomen; MRI, CT, ultrasound; cancer imaging 6. Anatomical shape analysis Methods: descriptors, learning shape models, 302225 hypothesis testing Application domains: brain, others 302225 7. Image registration Methods: similarity, transformation 302225 Applications: anatomical atlas, co- 302225 registration, motion correction 8. Content based medical-image retrieval Methods: image descriptors, image similarity 302225 Applications 302225The implementation-based experiments will rely on C/C++or Matlab environments. As part of the implementation-based experiments the students will be introduced tosoftware tools for medical image processing; examplesinclude popular open-source cross-platform softwarepackages for medical image analysis like the InsightToolkit (www.itk.org), Visualization Toolkit (www.vtk.org),etc., or other tools built using these packages. The coursewill use simulated and clinical medical image datasetsavailable freely through the Internet from universities orresearch institutions worldwide.
Last Update 08-07-2014 09:38:30.867808