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Image Registration Lecture 1: Introduction February 22, 2004

Image Registration Lecture 1: Introduction February 22, 2004. Prof. Charlene Tsai. http://www.cs.ccu.edu.tw/~tsaic/teaching/spring2005_grad/main.html. Outline. Syllabus Registration problem Applications of registration Components of a solution Thematic questions underlying registration

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Image Registration Lecture 1: Introduction February 22, 2004

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  1. Image Registration Lecture 1: IntroductionFebruary 22, 2004 Prof. Charlene Tsai http://www.cs.ccu.edu.tw/~tsaic/teaching/spring2005_grad/main.html

  2. Outline • Syllabus • Registration problem • Applications of registration • Components of a solution • Thematic questions underlying registration • Software toolkits Lecture 1

  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 Lecture 1

  4. Syllabus - Hours • Lecture hours: • Tuesday and Thursday • Office hours • By appointment Lecture 1

  5. Syllabus - Prerequisites • Data structures • Calculus • Linear algebra: • Vectors and matrices • Experience working with images • C++ programming experience • Templates! Lecture 1

  6. Syllabus - Requirements • 50% - Weekly homework assignments and programming projects (possibly 8) • 25% - Extended programming project (due before 23:59 of May 15) • 25% - 10-page research paper (due before 23:59 of June 15) • No examines! • Late assignments will not be accepted without prior arrangement or a verified personal emergency Lecture 1

  7. Syllabus - Course Materials • Powerpoint lectures will be placed on the course website • Software toolkits will include tutorials • Reading materials, mostly journal papers, will also be placed on the website • Most lecture slides by courtesy of Prof Chuck Stewart from RPI and Dr. Luis Ibanez from Kitware. Lecture 1

  8. Syllabus - Topics • Introduction • Mathematical background • First examples • Intensity-based registration and ITK • Feature-based registration and the RPI toolkit • Initialization techniques • Multiresolution techniques • Mutual information • Video registration and image mosaics • Deformable registration • Project presentation Lecture 1

  9. 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 Lecture 1

  10. q = (912,632) q = T(p;a) p = (825,856) Pixel location in first image Homologous pixel location in second image Pixel location mapping function Registration Problem Definition Lecture 1

  11. Example Mapping Function q = (912,632) p = (825,856) Pixel scaling and translation Lecture 1

  12. p = (825,856) Registration Problem Definition q = (912,632) q = T(p;q) • Problems: • Form of mapping function T • Unknown mapping parameters q • Unknown correspondences, p,q “Chicken-and-egg” problem Lecture 1

  13. 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 Lecture 1

  14. Example: MR-CT Brain Registration • 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 MR CT Source: http://www-ipg.umds.ac.uk/d.hill/hhh/10/10.pdf Lecture 1

  15. MR-CT Registration Results Superimposed images, with bone structures from CT in green Aligned images Lecture 1

  16. Retinal Angiogram and Color Image Lecture 1

  17. 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 Lecture 1

  18. Retinal Image Mosaics Lecture 1

  19. Sea-Floor Mosaics Courtesy Woods Hole Oceanographic Institution Lecture 1

  20. Spherical Mosaics Images from Sarnoff Corporation Lecture 1

  21. 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 Lecture 1

  22. Examples http://www1.cs.columbia.edu/~allen/NEW/workshop.html Lecture 1

  23. 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 Lecture 1

  24. Retinal Change Example Lecture 1

  25. Regions Showing Change Lecture 1

  26. Applications: Video Super-Imposed on 3d Model Taken from Sarnoff Corporation research Lecture 1

  27. 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 Lecture 1

  28. 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 Lecture 1

  29. 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 Lecture 1

  30. 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? Lecture 1

  31. Looking Ahead: Lecture 2 - Friday, January 16 • Mathematical background, part 1: • Vectors and matrices Lecture 1

  32. Homework Problem • Due Tuesday, March 1st before class (via email to me) • 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. Lecture 1

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