Special Topics CAP5937-Medical Image Computing (SPRING 2016)

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Special Topics CAP5937-Medical Image Computing (SPRING 2016)

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Instructor: Prof. Ulas Bagci    

Class time: Monday/Wednesday 10.30-11.45 am
Class location: HPA1 0106
Office hours: Monday/Wednesday 3-5 pm

COURSE GOALS: Imaging science is experiencing tremendous growth in the US. The New York Times recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as a primary reason for the growth. Biomedical imaging and its analysis are fundamental to understanding, visualizing, and quantifying medical images in clinical applications. With the help of automated and quantitative image analysis techniques, disease diagnosis will be easier/faster and more accurate, and leading to significant development in medicine in general. The goal of this course is to help students develop skills in computational radiology, radiological image analysis, and biomedical image processing fields. The following topics will be covered:

• Basics of Radiological Image Modalities and their clinical use
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and Signal Processing
• Medical Image Registration
• Medical Image Segmentation
• Medical Image Visualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images

PRE-REQUEST: Basic Probability/Statistics, a good working knowledge of any programming language (python, matlab, C/C++, or Java), Linear algebra, Vector calculus.

GRADING:Assignments and mini projects should include explanatory/clear comments as well as a short report describing the approach, detailed analysis, and discussion/conclusion.

• Programming assignments 30% (3 assignments, 10% each)
• Midterm 20%
• Project 50% (Presentation: 15%, Software/methods/results: 35%)

RECOMMENDED BOOKS (optional)

• Suetens, P. Fundamentals of Medical Imaging, Cambridge University Press
• Prince, J. & Links, J. Medical Imaging Signals and Systems, Prentice Hall,
• Bankman, Isaac.Handbook of Medical Imaging: Processing and Analysis, Academic Press,
• Yoo, Terry S. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, CRC Press,
• Sethian, J.A., Level-set Methods, Cambridge University Press, 2000,
• Duda, Hart and Stork, Pattern Classification (2nd Edition), Wiley, 2000,
• Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009,
• Strang, Gilbert. Linear Algebra and Its Applications 2/e, Academic Press, 1980.

PROGRAMMING
Students are enocouraged to use ITK/VTK programming libraries in implementation of the programming assignments and project.
ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis.
The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing, and visualization. It consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python.

Python and/or C/C++ can call functions of ITK/VTK easily. Matlab can be used for assignments as well.
Following book (Python programming samples for computer viion tasks) is freely available.
Python for Computer Vision

COLLABORATION POLICY
Collaboration on assignments is encouraged at the level of sharing ideas and technical conversation only. Please write your own code. Students are expected to abide by UCF Golden Rule.

LECTURE NOTES

• Lecture 1: Introduction to Medical Image Computing
• Lecture 2: Digital Images, Imaging Software, and Medical Imaging Modalities
• Lecture 3: Preprocessing Medical Images (I) - Definitions, Terminologies, Coordinate Systems, CAD, CAVA
• Lecture 4: Preprocessing Medical Images (II) - Smoothing and Inhomogeneity/Bias Correction
• Lecture 5: Preprocessing Medical Images (III) - MRI Intensity Standardization
• Lecture 6: Preprocessing Medical Images (IV) - Nuclear Medicine Imaging
• Lecture 7: Medical Image Segmentation (I) - Radiology Applications of Segmentation and Thresholding
• Lecture 8: Medical Image Segmentation (II) - Region Growing
• Lecture 9: Medical Image Segmentation (III) - Fuzzy Connectedness Image Segmentation
• Lecture 10: Medical Image Segmentation as an Energy Minimization Problem - MRF and Graph Cut
• Lecture 11: Active Contour and Level Set for Medical Image Segmentation
• Lecture 12: Active Shape Models and Oriented Active Shape Models
• Lecture 13: Clustering-based Segmentation Methods
• Lecture 14: Segmentation Evaluation Framework
• Lecture 15: Medical Image Registration I (Introduction)
• Lecture 16: Medical Image Registration II (Advanced)
• Lecture 17: Medical Image Registration III (Advanced)
• Lecture 18: Medical Image Visualization
• Lecture 19: Machine Learning in Medical Imaging I
• Lecture 20: Machine Learning in Medical Imaging II
• Lecture 21 and 22: fMRI Analysis
• Lecture 23 and 24: Diffusion Weighted MRI

PROGRAMMING ASSIGNMENTS

• Programming Assignment 1: Preprocessing framework for MR Brain Images
• Programming Assignment 2: Graph Cut Segmentation of Multi-Modal Thigh MR Images
• Programming Assignment 3: Deformable Image Registration of Chest CT Scans

POTENTIAL CLASS PROJECTS

• Lung Lobe Segmentation from CT Scans (Use LOLA11 Segmentation Challenge Data Set)
• Segmentation of Knee Images from MRI (Use SKI 2010 Data Set))
• Multimodal Brain Tumor Segmentation (Use BraTS Data Set)
• Automatic Lung Nodule (cancer) Detection (Use LUNA Data Set)
• Automatically measure end-systolic and end-diastolic volumes in cardiac MRIs. (Use Kaggle Cardiac Data Set)
• Head-Neck Auto Segmentation Challenge (Use MICCAI 2015 Segmentation Challange Data Set)
• CAD of Dementia from Structural MRI (Use MICCAI 2014 Segmentation Challenge Data Set)
• DTI Tractography Challenge (Use MICCAI 2014 Segmentation Challenge Data Set)
• EMPIRE 2010 - Pulmonary Image Registration Challenge (http://empire10.isi.uu.nl/index.php, I have the team name and password for downloading the data set).
• Digital Mammography DREAM challenge < LINK>
• MACHINE LEARNING Challenge in medical imaging < LINK>)

SELECTED PROJECTS FROM SPRING 2016 CLASS

will be updated soon...

Contact

Name:	Ulas Bagci
Email:	bagci@ucf.edu
URL:	http://www.cs.ucf.edu/~bagci
Work number:	(+1) 407-823-1047
Fax number:	(+1) 407-823-0594
CRCV Assistant:	Tonya LaPrarie tonya@crcv.ucf.edu
Mailing address:	Dr. Ulas Bagci Center for Research in Computer Vision (CRCV) 4328 Scorpius Street, HEC 221, UCF Orlando, Florida 32816, USA.

Last updated September, 2015 by Ulas Bagci.