[[Overview]] [[Location]] [[People]] [[Weekly Schedule]] [[Readings]] [[Projects]] [[Useful Links]] [[Announcements]]
@media screen\n{\n\nbody {\n font: 13px/125% "Lucida Grande", "Trebuchet MS", "Bitstream Vera Sans", Verdana, Helvetica, sans-serif;\n}\n\n}\n\n@media print\n{\n\nbody {\n font-size: 6.2pt;\n font-family: "Lucida Grande", "Bitstream Vera Sans", Helvetica, Verdana, Arial, sans-serif;\n}\n\n}
[[Comp Sc. & Stat Bldg.|http://www.fpm.wisc.edu/smomap/building.aspx?building=0155&wing=]] Room 1325 @ 9:30AM-10:45AM, Tuesdays & Thursdays.\n\nFor a campus map, go [[here|http://map.wisc.edu]]\n\n
config.options.chkHttpReadOnly = true;
!!!''Topics to be covered'' \n(this is only a first-draft and likely to change)\n\n#''Segmentation''\n##simple schemes (edge-based, threshold, region-grow, watershed,...)\n##EM, optical flow,...\n#''Representation and analysis'' \n##radial, polygonal, moments\n##skeletonization\n##graph-based data structures\n#''Registration''\n##simple methods for 2D\n##methods for rigid registration\n##Procrustes, ICPT\n##deformable contour models, level set methods\n#''Geometry/Topology''\n##Marching Cubes\n##AAM, ASM\n##multi-view geometry calibration,...\n#''Classification/Clustering''\n##intro to CAD\n##Representation and popular clustering schemes\n##Dimensionality reduction, near-neighbor searches, embedding\n##Support Vector Machines (?)\n#''Advanced Topics (?)''\n##Energy minimization and graph cuts, optimization methods\n##Delaunay triangulation and surface decimation\n##More to come...\n#''Presentations and Projects''\n\nProgramming component in C++ with VTK/ITK and Matlab.
[[Overview]]\n[[People]]\n
# Goals of Medical Image Analysis\n# Image processing basics\n# Brief history and Medical Imaging modalities\n# Problem Categories and description\n# Course Organization.\n\nSlides have been posted [1/25/2008].
#final portions of introduction\n#Image representation\n##Resolution and number of bits\n#Intensity, temporal and spatial sampling\n#Neighborhood system and connectivity\n#Regions and properties of regions\n#Convex hull construction (example: gift wrapping)\n
#Revision of Convex Hull\n#Graham Scan\n#Use of these ideas for perimeter extraction\n#Notion of distance and p-norms
#Boundary encoding\n##Chain codes, and others\n##Boundary simplification and smoothening\n##examples\n#Histograms\n##Histogram equalization process\n##examples on brain images\n#Data structures for images\n##Matrices\n##Graphs, examples\n
#Hierarchical data structures\n##M pyramids\n##T pyramids\n##Quad trees\n#Geometric transformations\n##Image rotation, translation etc.\n#Interpolation\n##Nearest neighbors and Linear\n
#Revision of rotation, translation, scaling etc.\n#Morphological operations\n##Erosion\n##Dilation\n##Opening\n##Closing\n##Medical examples\n#Segmentation\n#Threshold based\n#Choosing a threshold from histograms
!Anderson, Ashley \n!Barnard, Aubrey \n!Christensen, Matthew\n!Flink, Timothy \n!Frisch, Catherine \n!Gao, Yue\n!Han, Deok\n!Hurley, Samuel \n!Mclaren, Donald \n!Moirano, Jeffrey \n!Smith, Matthew \n!Tomkowiak, Michael \n!Wu, Mon Ju\n!Zakszewski, Elizabeth \n\n\n
!!''CS 638 Special Topics, Computational Methods in Medical Image Analysis (Spring 2008)''\n\n!!!''Overview''\n\nThis course will introduce us to medical image analysis algorithms. We will cover topics such as 3D reconstruction, registration, segmentation, classification and clustering in context of biomedical images. By the end of this course, we will gain a good understanding of the current problems in biomedical image analysis, the techniques employed to address such problems as well as outstanding research issues.\n\n!!!''Prerequisites''\n\nYou should be comfortable with a high level programming language (C, C++, or Java) or Matlab/Octave. Basic familiarity with linear algebra and calculus will be assumed. Come talk to me if you are in doubt about any other aspects of the course. \n\n!!!''Location/Times''\n\nThe class will meet in Comp Sc. & Stat Bldg. Room 1325 @ 9:30AM-10:45AM, Tuesdays & Thursdays.\n\n!!!''Grading''\n\nWill be based on programming assignments and/or homeworks, reading assignments, presentations, and individual or team projects. No exams.\n\nHere is a [[preliminary list of topics]] we plan to cover. Also see [[reference textbooks]] and [[Readings]].\n\nA shorter version of this course announcement can be found [[here|http://www.biostat.wisc.edu/~vsingh/courseDescription638.pdf]]\n\n\n
#Revision of Erosion and Dilation\n#Choosing Optimal Threshold\n##Mixture of Gaussians fit to histogram\n##Error minimization\n#Finding the number of modes in a histogram\n#Brief: Masking operations introduction
#Masking (intro)\n#Finding edges\n#Types of Masks\n##First order\n##Second order\n##LOG\n#Linking edges\n##Simple methods\n##Graph-based methods (Shortest path example)
#Revision of edge linking via graph search\n#Graph construction: edge weights\n#Live wires and examples\n#Hough transform\n##basic idea\n##pseudocode and examples\n##other representations, extensions beyond lines
#Region growing\n##Description\n##Examples\n#Watershed segmentation\n#Snakes\n##Energy minimization introduction\n##Background\n##Energy function construction
by Chris Hinrichs.\n\n#Expectation Maximization
#Review of Energy terms for Snakes\n#Gradient Descent method\n##Intro\n##Functions that can (and cannot) be efficiently minimized by GD.\n#Introduction to Dynamic Programming\n##Simple example (matrix)
#Review of DP\n#DP algorithms for snakes energy minimization\n##only elastic (pairwise) energy terms \n#DP framework for elastic and bending energy (Amini, Wemouth, Jain)
#Markov Random Field energies \n##brief introduction\n#Max Flow/Min Cut\n##Ford Fulkerson method\n###see handout in the files section
#Graph cut methods for MRF\n##graph construction and relation to energy function\n##alpha-beta swap\n##alpha expasion
#Introduction to image registration\n##Motivation \n##Types of registration\n#Rigid body registration with correspondences
#Brief introduction to eigen values/eigen vectors and decomposition\n#Rigid body registration based on moments and principal axes\n##Finding moments \n##Derivation of model for finding transformation\n#Introduction to Iterative Closest Point method
!Instructor\n\n''[[Vikas Singh| http://www.biostat.wisc.edu/~vsingh]]''\n\n7655 Medical Sciences Center\n1300 University Ave.,\nMadison, WI 53706.\n\nPhone: (608)262-8875.\nemail: username is vsingh, domain is biostat DOT wisc DOT edu\n\n''Office hours:'' Tuesdays/Thursdays, 4:30 - 6:30 PM. You are free to drop by at other times if I am in.\n\n!Teaching Assistant\n\n''[[Chris Hinrichs|http://www.cs.wisc.edu/~hinrichs]]''\n\n5390 CS & Statistics\n1210 W. Dayton St., \nMadison, WI 53706.\n\nPhone: (608)262-0018\nemail: username is hinrichs, domain is cs DOT wisc DOT edu\n\n''Office hours:'' Thursdays, 1:00 - 3:00 PM.\n\n
#Review of k-means\n##examples\n##problem cases and other local optimality/running time issues\n#Fuzzy variations\n##algorithms\n#Spectral methods for Clustering\n##Construction of similarity matrix\n##Motivation for ratio functional and relationship to graph cuts for MRF\n##Objective function \n##Constructing the Laplacian (PSD properties) and Matlab code example \n##Derivation of the generalized eigen value problem
#Review of implementation aspects of SIFT features\n#Applications in medical image registration\n##some brain imaging and other examples\n#Introduction to learning problems\n##Unsupervised and supervised clustering\n##distance measures and desirable qualities\n##Discussion on unsupervised \n###examples\n##Discussion on supervised\n##examples\n#k-means
Feature extraction\n\n#Introduction \n#Types of features to extract\n#Other properties\n#Harris corner detectors\n##Hessian and characteristics of its eigen values\n##medical image examples for registration\n##stability issues and limitations\n#SIFT features\n##introduction\n##key point extraction, difference of gaussians etc\n##orientation histograms\n##feature vector construction
#Splines\n##1-d case (interpolating)\n##bending energy\n##Natural Cubic splines and essential properties\n##value-second derivative specification and properties\n##smoothing splines\n#Thin Plate Splines\n##energy function\n##functional representation as affine and warp\n##motivating use in registration\n#Registration using TPS\n##energy function\n##solution strategies\n#Applications\n##animation\n##medical images examples\n
Guest lecture by Prof. Moo K. Chung on deformable registration and segmentation. \n\nSlides are available [[here|http://www.biostat.wisc.edu/~vsingh/teaching/files/mkchungCS638_08April.pdf]].\n\n
[[Class1]]\n[[Class2]]\n[[Class3]]\n[[Class4]]\n[[Class5]]\n[[Class6]]\n[[Class7]]\n[[Class8]]\n[[Class9]]\n[[Class10]]\n[[Class11]]\n[[Class12]]\n[[Class13]]\n[[Class14]]\n[[Class15]]\n[[Class16]]\n[[Class17]]\n[[Class18]]\n[[Class19]]\n[[Class20]]\n[[Class21]]\n[[Class22]]\n[[Class23]]\n[[Class24]]\n[[Class25]]\n[[Class26]]\n\nThe files section is [[here|http://www.biostat.wisc.edu/~vsingh/teaching/files]]
#Iterative Closest Point algorithm\n##Finding correspondences\n##Determining optimal transformation by SVD\n##kd trees discussion for closest point searches\n#Introduction to Mutual Information \n##Entropy
#Mutual information based registration\n##Entropy continued\n##Joint entropy\n##Mutual information as a function of entropy\n##Joint Image histograms\n##Optimization methods for MI registration\n##medical imaging examples \n#Deformable registration methods\n##Motivation\n##Introduction to Splines
!Matlab\n\n[[Quick overview |http://www.mathworks.com/access/helpdesk/help/techdoc/index.html?/access/helpdesk/help/techdoc/learn_matlab/bqr_2pl.html&http://www.cs.cmu.edu/afs/andrew/scs/cs/15-385/www/links.html]]\n[[A nice introduction to the Image Processing toolbox|http://www.mathworks.com/access/helpdesk/help/toolbox/images/images.shtml]]\n\n!C/C++/Java related\n\n[[CImg|http://cimg.sourceforge.net/]]\n[[VTK|http://www.vtk.org/]]\n[[ITK|http://www.itk.org/]]\n[[MIPAV|http://mipav.cit.nih.gov/index.php]]\n[[CGAL|http://www.cgal.org/]]\n[[LEDA|http://www.algorithmic-solutions.com/leda/ledak/index.htm]]\n\n!Other tools\n\n[[ImageJ|http://rsb.info.nih.gov/ij/]]\n[[Paraview|http://www.paraview.org/New/index.html]]\n[[GIMP|http://www.gimp.org/]]\n\n\n!Conferences and Journals of interest\n\n[[Transactions on Medical Imaging|http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=42]]\n[[Medical Image Analysis|http://www.elsevier.com/wps/find/journaldescription.cws_home/620983/description#description]]\n[[MICCAI|http://www.informatik.uni-trier.de/~ley/db/conf/miccai/index.html]]\n[[IPMI|http://www.informatik.uni-trier.de/~ley/db/conf/ipmi/index.html]]\n[[ISBI|http://www.biomedicalimaging.org/]]\n[[Medical Physics|http://scitation.aip.org/medphys/]]\n[[J. of MRI|http://www3.interscience.wiley.com/journal/10005199/home]]\n[[NeuroImage|http://www.sciencedirect.com/science/journal/10538119]]\n\n[[ICCV|http://www.informatik.uni-trier.de/~ley/db/conf/iccv/index.html]]\n[[CVPR|http://www.informatik.uni-trier.de/~ley/db/conf/cvpr/index.html]]\n[[ECCV|http://www.informatik.uni-trier.de/~ley/db/conf/eccv/index.html]]\n[[International J. of Computer Vision|http://www.springerlink.com/content/0920-5691]]\n[[Pattern Recognition|http://www.informatik.uni-trier.de/~ley/db/journals/pr/index.html]]\n[[PAMI|http://www.computer.org/portal/site/transactions/menuitem.802944db300bb678c4f34b978bcd45f3/index.jsp?&pName=tpami_home&]]\n[[NIPS|http://books.nips.cc/]]
#Review of spectral clustering ideas from last class\n#Normalized cuts\n##relaxation from discrete problem\n##solution methods and the generalized eigen value problem\n#Spectral clustering (Ng, Jordan, Weiss)\n##ideas and methods\n#Examples in diffusion tensor imaging (tracts clustering), other application, also segmentation\n#k-nearest neighbor classification\n##methods\n##data-structures\n##examples\n#Perceptrons\n#Similarity, kernels, and dot-product spaces \n#Parzen windows classifier\n#Introduction to SVM\n
#Review of Parzen window classifiers\n#Margin maximization for classification\n#Feature space transformation\n##Examples\n##Matlab demo\n#Kernel trick\n#Calculating the margin\n##Derivation of the model\n#The dual model\n##Lagrangian\n##Deriving the model and KKT conditions interpretation\n##Examples in medical imaging\n##Biomedical imaging examples in the popular media
#First class meets on 01/22/2008, Tuesday\n#Lecture 1 Slides have been posted [1/25/2008].\n#''Assignment 1 has been posted'' [[here|http://www.biostat.wisc.edu/~vsingh/teaching/files/hw1.pdf]] [02/05/2008]. This assignment accounts for up to 10% of your grade. The assignment is due Feb 20, 2008 11:59PM. \n#''Course project proposals are due ==Feb 15, 2008== Feb 22, 2008''. [02/05/2008] \n#Lecture 2 Slides have been posted [02/07/2008].\n#Link to files section updated in Weekly Schedule [02/07/2008]. \n#Temporary version of Lecture 3 slides posted [2/12/2008].\n#Temporary version of Lecture 3 slides posted [2/23/2008].\n#Homework 1 grades sent [3/05/2008].\n#Lecture 3 Slides have been posted [03/06/2008].\n#A figure for Max-flow construction discussed on blackboard is posted [03/13/2008].\n#''Assignment 2 has been posted'' [[here|http://www.biostat.wisc.edu/~vsingh/teaching/files/hw2.pdf]] [03/13/2008]. This assignment accounts for up to 10% of your grade. The assignment is due ''Apr 4, 2008'' 11:59PM. \n#Skeletal code available from Chris [03/16/2008].\n#Temporary version of Lecture 4 slides posted [3/27/2008].\n#Matlab code for eigen values animation posted [3/27/2008].\n#Slides from Prof. Moo K. Chung's guest lecture posted [[here|http://www.biostat.wisc.edu/~vsingh/teaching/files/mkchungCS638_08April.pdf]] [04/08/2008].\n#Lecture 4 Slides have been posted [4/10/2008].\n#Lecture 5 Slides have been posted [4/16/2008].\n#''Assignment 3 has been posted'' [[here|http://www.biostat.wisc.edu/~vsingh/teaching/files/hw3.pdf]] [04/20/2008]. This assignment accounts for up to 10% of your grade. The assignment is due ==Apr 30, 2008== ''May 2, 2008'' 11:59PM. \n#''Project reports due on 5/13/2008''. [4/23/2008].\n#''Project presentations start on 05/01/2008''. [4/24/2008].\n#Assignment 3 due date extended, see above [4/28/2008].\n#Lecture 6 Slides have been posted [4/29/2008].
There are no required textbooks. However, you may find some of the topics in the following books useful.\n\n#Atam P. Dhawan, Medical Image Analysis \n#Rangaraj M. Rangayyan, Biomedical Image Analysis. \n#N Paragios, Y Chen, O Faugeras, Handbook of Mathematical Models in Computer Vision\n#Insight into Images: Principles & Practice for Segmentation, Registration and Image Analysis, Terry S. Yoo.\n#Image Processing, Analysis, and Machine Vision, M. Sonka, V. Hlavac, and R. Boyle.
[[Intelligent Scissors for Image Composition|http://www.biostat.wisc.edu/~vsingh/teaching/files/scissors_comp.pdf]], E. N. Mortensen and W. A. Barrett, SIGGRAPH 1995. \n\n[[Dynamic programming for detecting, tracking, and matching deformable contours|http://www.biostat.wisc.edu/~vsingh/teaching/files/geiger.pdf]], D. Geiger, A. Gupta, L. A. Costa, and J. Vlontzos, IEEE Tran. on Pattern Analysis and Machine Intelligence 1995.\n\n[[Geodesic active contours|http://www.biostat.wisc.edu/~vsingh/teaching/files/caselles95geodesic.pdf]], V. Caselles, R. Kimmel, and G. Sapiro, International Journal of Computer Vision 1997.\n\n[[Region tracking on level-sets methods|http://www.biostat.wisc.edu/~vsingh/teaching/files/bertalmio.pdf]], M. Bertalmio, G. Sapiro, and G. Randall, IEEE Tran. Medical Imaging 1999.\n\n[[An efficient algorithm for image segmentation, Markov Random Fields and related problems|http://www.biostat.wisc.edu/~vsingh/teaching/files/segment.pdf]], D. Hochbaum, Journal of ACM 2001.\n\n[[Object recognition from local scale-invariant features|http://www.biostat.wisc.edu/~vsingh/teaching/files/sift.pdf]], D. G. Lowe, International Conference on Computer Vision 1999. \n\n[[Fast Approximate Energy Minimization via Graph Cuts|http://www.biostat.wisc.edu/~vsingh/teaching/files/BVZ-pami01.pdf]], Y. Boykov, O. Veksler, and R. Zabih. IEEE Tran. on Pattern Analysis and Machine Intelligence 2001.\n\n[[Normalized Cuts and Image Segmentation|http://www.biostat.wisc.edu/~vsingh/teaching/files/SM-ncut.pdf]], J. Shi and J. Malik, IEEE Tran. on Pattern Analysis and Machine Intelligence 2000.\n\n[[User-Steered Image Segmentation Paradigms: Live Wire and Live Lane|http://www.ingentaconnect.com/content/ap/ip/1998/00000060/00000004/art00475]], A. X. Falcao, J. K. Udupa, S. Samarasekera, S. Sharma, B. E. Hirsch, A.D.R. Lotufo, Graphical Models and Image Processing 1998.\n\n[[Predicting error in rigid-body point-based registration|http://www.biostat.wisc.edu/~vsingh/teaching/files/fitzpatrick.pdf]], J. M Fitzpatrick, J. B West, C. R Maurer Jr., IEEE Tran. on Medical Imaging 1998.\n\n[[A new point matching algorithm for non-rigid registration|http://www.biostat.wisc.edu/~vsingh/teaching/files/rangarajan.pdf]], H. Chui and A. Rangarajan, Computer Vision and Image Understanding 2003. \n\n[[Landmark Matching Via Large Deformation Diffeomorphisms|http://www.biostat.wisc.edu/~vsingh/teaching/files/Joshi_TIP_2000.pdf]], S. Joshi and M. Miller, IEEE Tran. on Image Processing 2000.\n\n[[Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation|http://www.biostat.wisc.edu/~vsingh/teaching/files/levelset_ijcv02.pdf]], N. Paragios and R. Deriche, International Journal of Computer Vision 2002. \n\n[[Multimodality Image Registration by Maximization of Mutual Information|http://www.biostat.wisc.edu/~vsingh/teaching/files/maes.pdf]], F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, IEEE Tran. on Medical Imaging 1997.\n\n[[Mutual information based registration of medical images|http://www.biostat.wisc.edu/~vsingh/teaching/files/mutual_info_survey.pdf]], J. P. W. Pluim, J. B. A. Maintz, and M. Viergever, IEEE Tran. on Medical Imaging 2003. \n\n[[Snakes: Active Contour Models|http://www.biostat.wisc.edu/~vsingh/teaching/files/snakesTerzopoulos.pdf]], M. Kass, A. Witkin, and D. Terzopoulos, International Journal of Computer Vision 1988.\n\n[[Active appearance models|http://www.biostat.wisc.edu/~vsingh/teaching/files/cootes.pdf]], T. F. Cootes, G. J. Edwards, and C. J. Taylor, IEEE Tran. on Pattern Analysis and Machine Intelligence 2001. \n\n[[Deformable models in medical image analysis: a survey|http://www.biostat.wisc.edu/~vsingh/teaching/files/survey_deformable.pdf]], T. McInerney and D. Terzopoulos, Medical Image Analysis 1996. \n\n[[Marching Cubes : A High Resolution 3D Surface Construction Algorithm|http://www.biostat.wisc.edu/~vsingh/teaching/files/lorensen.pdf]], W. Lorensen and H. Cline, ACM SIGGRAPH 1987.\n\n[[Medical Image Registration|http://www.biostat.wisc.edu/~vsingh/teaching/files/hill.pdf]], D. Hill, P. Batchelor, M. Holden, and D. J. Hawkes, Phys. Med. Biol. 2001.\n\n[[Image Registration|http://www.biostat.wisc.edu/~vsingh/teaching/files/registration_chapter.pdf]], J. M. Fitzpatrick, D. Hill, C. R. Maurer Jr. (book chapter).\n\n[[Medical image analysis: progress over two decades and the challenges ahead|http://www.biostat.wisc.edu/~vsingh/teaching/files/duncan.pdf]], J. Duncan and N. Ayache, IEEE Tran. on Pattern Analysis and Machine Intelligence 2000. \n\n[[HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration|http://www.biostat.wisc.edu/~vsingh/teaching/files/hammer.pdf]], D. Shen and C. Davatzikos, IEEE Tran. on Medical Imaging 2002. \n\n[[Snakes, Shapes, and Gradient Vector Flow|http://www.biostat.wisc.edu/~vsingh/teaching/files/xuprince.pdf]], C. Xu and J. L. Prince, IEEE Tran. on Image Processing 1998.\n\n[[Edge Detection and Ridge Detection with Automatic Scale Selection|http://www.biostat.wisc.edu/~vsingh/teaching/files/lindeberg.pdf]], T. Lindeberg, International Journal of Computer Vision 1998.\n\n[[A Survey of Medical Image Registration|http://www.biostat.wisc.edu/~vsingh/teaching/files/maintz.pdf]], J. B. A. Maintz and M. A. Viergever, Medical Image Analysis 1998.\n\n[[Shape modeling with front propagation: a level set approach|http://www.biostat.wisc.edu/~vsingh/teaching/files/malladi.pdf]], R. Malladi, J. Sethian, B. Vemuri, IEEE Tran. on Pattern Analysis and Machine Intelligence 1995. \n\n[[Optimum Image Thresholding via Class Uncertainty and Region Homogeneity|http://www.biostat.wisc.edu/~vsingh/teaching/files/saha.pdf]], P. K. Saha and J. K. Udupa, IEEE Tran. on Pattern Analysis and Machine Intelligence 2001. \n\n[[Efficient multilevel image thresholding|http://www.biostat.wisc.edu/~vsingh/teaching/files/multilevelImageThresholding.pdf]], M. Eichmann, M. Lussi, 2005. \n\n[[Minimum error thresholding|http://portal.acm.org/citation.cfm?id=20363]], J. Kittler and J. Illingworth, Pattern Recognition 1986.\n\n[[A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models|http://www.biostat.wisc.edu/~vsingh/teaching/files/blimes.pdf]], J. A. Blimes, ICSI Technical Report 1997.\n\n[[Mean shift, mode seeking and clustering|http://www.biostat.wisc.edu/~vsingh/teaching/files/cheng.pdf]], Y. Chen, IEEE Tran. on Pattern Analysis and Machine Intelligence 1995.\n\n[[Mean Shift Analysis and Applications|http://www.biostat.wisc.edu/~vsingh/teaching/files/comaniciu.pdf]], D. Comaniciu and P. Meer, International Conference on Computer Vision 1999. \n\n[[A method for the registration of 3-D shapes|http://www.biostat.wisc.edu/~vsingh/teaching/files/besl.pdf]], P. Besl and N. McKay, IEEE Tran. on Pattern Analysis and Machine Intelligence 1992.\n\n[[Multi-resolution elastic matching|http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B7GXG-4KFT8TY-2&_user=443835&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000020958&_version=1&_urlVersion=0&_userid=443835&md5=34cd2a29428c3c53bb5a4891dad2a337]], R. Bajcsy and S. Kovacic, Computer Vision, Graphics and Image Processing 1989. \n\n[[Understanding the Demon's Algorithm: 3D Non-rigid Registration by Gradient Descent|http://www.biostat.wisc.edu/~vsingh/teaching/files/pennec.pdf]], X. Pennec, P. Cachier, and N. Ayache, Medical Image Computing and Computer-Assisted Intervention 1999. \n\n