CSE6389 Medical Image Analysis (Spring 2008)

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CSE6389 Medical Image Analysis (Spring 2008)

Heng Huang

[ Administrative Basics | Course Description | Assignments | Outline of Lectures ]

Administrative Basics

Lecture	Nedderman Hall 229 \| Mondays and Wednesdays 4:00-5:20 PM
Instructor	Heng Huang \| Nedderman Hall 308 \| Office hours: Mondays and Wednesdays 2:00-3:30 PM
Textbook	Required: none Recommended: Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. Edited by Terry S. Yoo. August 2004; ISBN 1-56881-217-5 . Good books for reading: Foundations of Medical Imaging, Z. H. Cho, Joie P. Jones, Manbir Singh. 1993; ISBN: 978-0-471-54573-6. Digital Image Processing. Rafael C. Gonzalez, Richard E. Woods. 2002; ISBN: 0201180758 .
Work	Two homework sets. (20%) Class presentations. (20%) One medical image analysis project (a list of project candidates will be posted). (40%) Participation. (20%)
Request	Basic math and programming background, for undergraduate student, CSE 2320, CSE 4303 or CSE 4313, are requested

Course Description

The aim of the course is to show how computer science can be used to model and analyze medical data in order to provide a prognosis and develop cures. Medical image computing is, by nature, an interdisciplinary field involving not only medicine but also computer science, mathematics, biology, psychology, statistics and other fields. The "glue" to all is computer science which can help detect patterns and make sense out of disparate types of information.

The course is application-driven and includes topics in medical image analysis, including image segmentation, registration, statistical modeling and applications of computational science in enabling treatment. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The course will provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the university.

Assignments

Homework 1 (Please send me your solutions of HW1 before our class in Mar. 26)
Homework 2 (Please send me your solutions of HW2 before our class in Apr. 21)

Final Project

Project Requirements:
MATLAB or C - Programming language
Writing code by yourself

Project Descriptions: (please select one from the follows)

1) Segmentation project. Writing code to segment medical images using "snakes"
Snakes are computer-generated curves that move within images to find object boundaries. They are often used in medical image analysis to detect and locate objects, and to describe their shape. Rough shape and starting position of the snake are specified by the user. This is done by clicking a few points on the image which become the vertices of the initial snake. The snake is defined as an energy minimising contour where the energy function is a combination of internal and external forces. An iterative procedure causes the snake to shrink, reduce its total curvature and move towards interesting objects in image. In our class, we have studied the regular snake method. Please use MATLAB or C to implement the regular snake method for medical image segmentation. The testing medical images will be sent to students by email.

2) Registration project. Using SIFT method to do biomedical image registration. The SIFT method was introduced in our class and the references are [1], [2], [3].
Perfusion Magnetic resonance imaging is an imaging technique used for measuring blood flow through organs, tissues, and vessels. While the scanner continuously acquires data, a paramagnetic contrast agent is injected and perfuses through the patient's lung. Perfusion MR Image sequences often suffers from motion induced by breathing during image acquisition. To ensure the corresponding anatomical structures in different time frames can be properly compared, registration of pMRI sequence is necessary. Please apply SIFT method onto perfusion MR Images registration.

Outline of Lectures

Week 1.	Mon Jan 14: Introduction to Medical Image Processing, Course Objectives Wed Jan 16: Basic Image Processing, Linear Operators
Week 2.	Mon Jan 21: No class, Martin Luther King Jr. Day holiday Wed Jan 23: Discrete Fourier, Discrete Cosine Transforms. Reading materials: [1]
Week 3.	Mon Jan 28: Physics of Medical Imaging 1 Reading materials: The Basics of MRI Wed Jan 30: Physics of Medical Imaging 2
Week 4.	Mon Feb 04: Physics of Medical Imaging 3
Week 5.	Mon Feb 11: Statistical Pattern Recognition 1 Wed Feb 13: Statistical Pattern Recognition 2
Week 6.	Mon Feb 18: PCA, SVD, and PDE-based Nonlinear Image Filtering Wed Feb 20: Segmentation for Medical Images 1
Week 7.	Mon Feb 25: Segmentation for Medical Images 2 Wed Feb 27: Segmentation for Medical Images 3
Week 8.	Mon Mar 03: Segmentation for Medical Images 4 Wed Mar 05: Medical Images Registration 1 (Intensity-Based Registration)
Week 9.	Mon Mar 10: Medical Images Registration 2 (Feature-Based Registration) Wed Mar 12: Medical Images Registration 3 (Robust Estimation)
Week 10.	Spring Break
Week 11.	Mon Mar 24: Medical Images Registration 4 (Deformable Registration) Wed Mar 27: Medical Images Registration 5 (Scale Invariant Feature Transformation) Reading materials: SIFT by Lowe published in IJCV 2004
Week 12.	Mon Mar 31: Visualization 1 (Scalar Visualization) Reading materials: Marching Cubes: A High Resolution 3D Surface Construction Algorithm The Asymptotic Decider: Removing the Ambiguity in Marching Cubes Wed Apr 2: Visualization 2 (Volume Visualization) Reading materials: Back-to-Front Display of Voxel-Based Objects H. Tuy and L. Tuy. Direct 2D Display of 3D Objects. IEEE Computer Graphics and Applications, 4(10):29--33, 1984 (please look at this paper at library).
Week 13.	Mon Apr 7: Statistical Shape Modeling 1 Wed Apr 10: Statistical Shape Modeling 2
Week 14.	Mon Apr 14: Diffusion Tensor (DT) MRI 1 Wed Apr 16: Diffusion Tensor (DT) MRI 2
Week 15.	Mon Apr 21: Class presentations An Adaptive Level Set Method for Medical Image Segmentation. (Rob Freund) Gray Scale Registration of Mammograms Using a Model of Image Acquisition. (Sajal Chirvi) CLASSIC: Consistent Longitudinal Alignment and Segmentation for Serial Image Computing. (Sameer Dhamne) Wed Apr 23: Class presentations Multi-fiber Reconstruction from Diffusion MRI Using Mixture of Wisharts and Sparse Deconvolution. (Hua Wang) Kernel-Based Manifold Learning for Statistical Analysis of Diffusion Tensor Images. (Dijun Luo)
Week 16.	Mon Apr 28: Class presentations Multiscale Vessel Enhancing Diffusion in CT Angiography Noise Filtering. (Nha Nguyen) An inverse method for the recovery of tissue parameters from colour images. (Jyoti Bhat) Wed Apr 30: Biomedical Image Applications

Paper List for Presentation