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CSci 6971: Image Registration Lecture 1: Introduction January 13, 2004

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Title: CSci 6971: Image Registration Lecture 1: Introduction January 13, 2004


1
CSci 6971 Image Registration Lecture 1
Introduction January 13, 2004
Prof. Chuck Stewart, RPI Dr. Luis Ibanez, Kitware
2
Outline
  • Syllabus
  • Registration problem
  • Applications of registration
  • Components of a solution
  • Thematic questions underlying registration
  • Software toolkits

3
Syllabus - Topic
  • Image registration
  • Determining the mapping between two images of the
    same object, similar objects, the same region or
    similar regions
  • All aspects of the problem will be covered
  • Underlying mathematics
  • Images
  • Algorithms
  • Implementations
  • Applications
  • Special emphasizis on software toolkits

4
Syllabus - Instructors
Prof. Chuck Stewart 107 Amos Eaton Rensselaer
Polytechnic Institute 518-276-6731 stewart_at_cs.rpi.
edu
Dr. Luis Ibanez Kitware Corporation
(518)-371-3971 x 112 luis.ibanez_at_kitware.com
5
Syllabus - Distributed Course
  • NSF Center for Subsurface Sensing and Imaging
    Systems (CenSSIS) Course
  • Four universities
  • Boston University,
  • Northeastern University,
  • Rensselaer Polytechnic Institute,
  • University of Puerto Rico Mayaguez
  • Lectures live at Rensselear
  • Lectures recorded in voice-annotated powerpoint
    for remote students
  • Lectures missed due to weather or travel will
    also be available via voice-annotated powerpoint

6
Syllabus - Office Hours
  • At Rensselaer
  • Tuesday and Friday immediately following lecture
  • Distributed office hours
  • Web-meeting
  • Anyone can participate

7
Syllabus - Prerequisites
  • Data structures
  • Calculus
  • Linear algebra
  • Vectors and matrices
  • Experience working with images
  • C programming experience
  • Templates!

8
Syllabus - Requirements
  • Weekly homework assignments and programming
    projects
  • Extended programming project (due Tuesday, April
    6)
  • 10-page research paper (due Tuesday, May 4)
  • Each is worth 30 of the semester grade
  • Late assignments will not be accepted without
    prior arrangement or a verified personal
    emergency
  • Last 10 is for acting as a scribe during
    lecture. This is a pass/fail requirement that
    off-campus students automatically pass.

9
Syllabus - Course Materials
  • Voice-annotated powerpoint lectures will be
    placed on CenSSIS website
  • Software toolkits will include tutorials
  • Reading materials will also be placed on the
    CenSSIS website

10
Syllabus - Topics
  • Introduction
  • Mathematical background
  • First examples
  • Intensity-based registration and ITK
  • Feature-based registration and the CenSSIS/RPI
    toolkit
  • Initialization techniques
  • Multiresolution techniques
  • Mutual information
  • Deformable registration
  • Video registration and image mosaics
  • Trends and research questions

11
Syllabus - Academic Integrity
  • Students may discuss homework and programming
    assignments
  • Solutions must be written in students own words
  • Extended programming project and research paper
    must be individual work with appropriate
    citations
  • A serious incident will result in failing the
    course

12
Registration Problem Definition
13
Example Mapping Function
q (912,632)
p (825,856)
Pixel scaling and translation
14
Registration Problem Definition
q (912,632)
q T(pq)
  • Problems
  • Form of mapping function T
  • Unknown mapping parameters q
  • Unknown correspondences, p,q

Chicken-and-egg problem
15
Applications Multimodal Integration
  • Two or more different sensors view same region or
    volume
  • Different viewpoints
  • (Some specialized sensors have two or more
    coincident modalities, so registration is not
    needed.)
  • Different information is prominent in each image
  • The images may even have different dimensions!
  • Range images vs. intensity images
  • CT volumes vs. fluoro images

16
Example MR-CT Brain Registration
MR
CT
  • MR (magnetic resonance) measures water content
  • CT measures x-ray absorption
  • Bone is brightest in CT and darkest in MR
  • Both images are 3d volumes

Source http//www-ipg.umds.ac.uk/d.hill/hhh/10/10
.pdf
17
MR-CT Registration Results
Superimposed images, with bone structures from CT
in green
Aligned images
18
Retinal Angiogram and Color Image
19
Applications Image Mosaics
  • Many, partially overlapping images
  • No one gives a complete view
  • Goal stitch images together
  • Requires
  • Limited camera viewpoint such as rotation about
    optical center
  • Simple surface geometry such as plane or
    quadratic

20
Retinal Image Mosaics
21
Sea-Floor Mosaics
Courtesy Woods Hole Oceanographic Institution
22
Spherical Mosaics
Images from Sarnoff Corporation
23
Applications Building 3d Models
  • Range scanners store an (x,y,z) measurement at
    each pixel location
  • Each range image gives a partial view
  • Must register range images and texture map them
  • Applications
  • Reverse engineering
  • Digital architecture and archaeology

24
Examples
http//www1.cs.columbia.edu/allen/NEW/workshop.ht
ml
25
Applications Change Detection
  • Images taken at different times
  • Following registration, the differences between
    the images may be indicative of change
  • Deciding if the change is really there may be
    quite difficult

26
Retinal Change Example
27
Regions Showing Change
28
Applications Video Super-Imposed on 3d Model
Taken from Sarnoff Corporation research
29
Other Applications
  • Multi-subject registration to develop organ
    variation atlases.
  • Used as the basis for detecting abnormal
    variations
  • Object recognition - alignment of object model
    instance and image of unknown object
  • Industrial inspection
  • Compare CAD model to instance of part to
    determine errors in manufacturing process

30
Steps Toward a Solution
  • Analyze the images
  • Determine the appropriate image primitives
  • Determine the transformation model
  • Geometric and intensity
  • Design an initialization technique
  • Develop constraints and an error metric on the
    transformation estimate
  • Design a minimization algorithm
  • Develop a convergence criteria

31
Software Toolkits
  • ITK
  • Medical image processing, segmentation, and
    registration toolkit
  • C, heavily templated, data flow architecture
  • Registration stresses intensity-based approaches
  • VXL
  • Computer vision applications
  • C, moderate templating
  • Registration stresses feature-based approaches
  • CenSSIS tool suite is a hybrid of these
  • They can be used together
  • Programming styles are different

32
Summary Pervasive Questions
  • Three questions to consider in approaching any
    registration problem
  • What intensity information or image structures
    is/are consistent between the images to be
    registered?
  • What is the geometric relationship between the
    image coordinate systems?
  • What prior information can be used to constrain
    the domain of possible transformations?

33
Looking Ahead Lecture 2 - Friday, January 16
  • Mathematical background, part 1
  • Vectors and matrices
  • No meeting on Rensselaer campus
  • Voice-annotated lecture will be posted Thursday
    night, January 15

34
Homework Problem
  • Due Tuesday, January 20 at 12 noon (via email to
    Professor Stewart)
  • Problem
  • Find an application of registration, preferably
    in a research area of interest to you. In a
    short write-up (less than a full page), describe
    the problem and attempt to sketch answers to the
    three Pervasive Questions posed.
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