CAP5937-Medical Image Computing (SPRING 2017)
Class time: Monday/Wednesday 10.30-11.45 am
Class location: Eng1 0286
Office hours: Monday/Wednesday 1-2.30 pm, in my office HEC221.
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:
Basic Probability/Statistics, a good working knowledge of any programming language (python, matlab, C/C++, or Java), Linear algebra, Vector calculus.
- 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
- Shape Modeling/Analysis of Medical Images
- Machine Learning/Deep Learning in Medical Imaging
- NeuroImaging: fMRI, DTI, MRI, Connectome
GRADING:Assignments and the project should include explanatory/clear comments as well as a short report describing the approach, detailed analysis, and discussion/conclusion.
RECOMMENDED BOOKS (optional)
- Programming assignments 30% (3 assignments, 10% each)
- In-class quizzes (20%, approximately 20 quizzes will be distributed, each 1% weight, each quiz will include only 1 question)
- Project 50% (Presentation: 15%, Software/methods/results: 35%)
- 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.
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 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.
POTENTIAL INDIVIDUAL PROJECTS
- Try to choose something from MICCAI 2017 Challenges
- 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>)
| Mailing address:
Dr. Ulas Bagci
Center for Research in Computer Vision (CRCV)
4328 Scorpius Street, HEC 221, UCF
Orlando, Florida 32816, USA.
Last updated March, 2017 by Ulas Bagci.