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MEDICAL IMAGE COMPUTING (CAP 5937)- SPRING 2016

LECTURE 1: Introduction

Dr. Ulas Bagci

HEC 221, Center for Research in Computer Vision
(CRCV), University of Central Florida (UCF),
Orlando, FL 32814.
bagci@ucf.edu or bagci@crcv.ucf.edu
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• This is a special
topics course,
offered for the first
time in UCF.

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CAP5937: Medical Image Computing

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• This is a special
topics course,
offered for the first
time in UCF.
• Lectures:
Mon/Wed, 10.30am-
11.45am
Lorem Ipsum Dolor Sit Amet

CAP5937: Medical Image Computing

 4

• This is a special
topics course,
offered for the first
time in UCF.
• Lectures:
Mon/Wed, 10.30am-
11.45am
• Office hours:
Lorem Ipsum Dolor Sit Amet
Mon/Wed, 1pm-
2.30pm
CAP5937: Medical Image Computing
 5

• This is a special topics

course, offered for the
first time in UCF.
• Lectures: Mon/Wed,
10.30am-11.45am
• Office hours:
Mon/Wed, 1pm-
2.30pm
Lorem Ipsum Dolor Sit Amet • No textbook is
required, materials will
be provided.
CAP5937: Medical Image Computing
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Image
Processing Computer
Vision

Medical
Image
Imaging Computing
Sciences
(Radiology,
Biomedical) Machine
Learning
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Motivation
• Imaging sciences is experiencing a tremendous
growth in the U.S. The NYT recently ranked
biomedical jobs as the number one fastest growing
career field in the nation and listed bio-medical
imaging as the primary reason for the growth.
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Motivation
• Imaging sciences is experiencing a tremendous
growth in the U.S. The NYT recently ranked
biomedical jobs as the number one fastest growing
career field in the nation and listed bio-medical
imaging as the primary reason for the growth.
• Biomedical imaging and its analysis are fundamental
to (1) understanding, (2) visualizing, and (3)
quantifying information.
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Motivation
• Imaging sciences is experiencing a tremendous
growth in the U.S. The NYT recently ranked
biomedical jobs as the number one fastest growing
career field in the nation and listed bio-medical
imaging as the primary reason for the growth.
• Biomedical imaging and its analysis are fundamental
to (1) understanding, (2) visualizing, and (3)
quantifying information.
• This course will mostly focus on analysis of
biomedical images, and imaging part will be briefly
taught!
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Syllabus
• Basics of Radiological Images, Imaging, and Their
Clinical Use
– X-Ray, CT, MRI, fMRI, DTI, DWI, PET, dPET, PET/CT,
MRI/PET,…
• Image Enhancement and Pre-processing
– Spatial and Frequency Domain Filtering
• Medical Image Registration/Alignment
– Atlas construction, disease tracking, severity analysis,…
• Medical Image Segmentation
– Extraction of object information, volumetry, morphometry,..
• Medical Image Visualization
• Machine Learning for Medical Imaging
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Syllabus
• Grading:
– 1 Mid-term exam at the classroom, written (20%)
– 3 Programming Assignments (each 10%, total 30%)
• ITK/VTK packages should be used
• ITK and VTK provide necessary codes/libraries for medical
image processing and analysis.
• C/C++ or Python can be used and call ITK/VTK functions
• In-class collaboration is encouraged, but individual submission
is required.
– 1 Individual Project (50%)
• Will be selected from a list of projects
• A short presentation (15%), coding/method (25%), results
(10%)
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Optional Reading List

• Image Processing, Analysis, and Machine Vision. M. Sonka, V.
Hlavac, R. Boyle. Nelson Engineering, 2014.
• Level-set Methods, by J. A. Sethian, Cambridge University Press.
• Visual Computing for Medicine: Theory, Algorithms, and
Applications. B. Preim, C. Botha. Morgan Kaufmann, 2013.
• Medical Image Registration. J. Hajnal, D. Hill, D. Hawkes (eds).
CRC Press, 2001.
• Pattern Recognition and Machine Learning. C. Bishop. Springer,
2007.
• Insight into Images: Principles and Practice for Segmentation,
Registration and Image Analysis, Terry S. Yoo (Editor) (FREE)
• Algorithms for Image Processing and Computer Vision, J. R. Parker
• Medical Imaging Signals and Systems, by Jerry Prince & Jonathan
Links, Publisher: Prentice Hall
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Conferences and Journals to Follow

• The top-tier conferences (double blind, acceptance rates are below
25%, high quality technical articles):
– MICCAI (medical image computing & computer assisted intervention)
– IPMI (Information Processing in Medical Imaging)
– Other conferences: IEEE ISBI, EMBC and SPIE Med Imaging
– Clinical Conferences: RSNA (>65.000 attendances), ISMRM, SNM
• The top-tier technical journals:
– IEEE TMI, TBME, PAMI, and TIP
– Medical Image Analysis, CMIG, and NeuroImage
• The top-tier clinical journals relevant to MIC:
– Radiology, Journal of Nuclear Medicine, AJR, Nature Methods, Nature
Medicine, PlosOne, …
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Required skill set

• Basic programming experience (any language is fine)
• Linear Algebra/Matrix Algebra
• Differential Equations
• Basic Statistics
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Biomedical Images
• (Bio)medical images are different from other
pictures
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Biomedical Images
• (Bio)medical images are different from other
pictures
– They depict distributions of various physical features
measured from the human body (or animal).
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Biomedical Images
• (Bio)medical images are different from other
pictures
– They depict distributions of various physical features
measured from the human body (or animal body).
• Analysis of biomedical images is guided by very
specific expectations
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Biomedical Images
• (Bio)medical images are different from other pictures
– They depict distributions of various physical features
measured from the human body (or animal).
• Analysis of biomedical images is guided by very
specific expectations
– Automatic detection of tumors, characterizing their types,
– Measurement of normal/abnormal structures,
– Visualization of anatomy, surgery guidance, therapy
planning,
– Exploring relationship between clinical, genomic, and
imaging based markers
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Free Software to Use in this course

• ImageJ (and/or FIJI)
• ITK-Snap
• SimpleITK
• MITK
• FreeSurfer
• SLICER
• OsiriX
• An extensive list of software: www.idoimaging.com and

blue: will be frequently used in this course

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• Thank you for your attention!