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Registration of MR Images

Registration of MR Images. Master Thesis By Naga Padma Krishnam Raju Dandu. Supervisor: Ole Fogh Olsen, associate professor, IT University of Copenhagen. Table of Contents. Introduction Objective Registration Rigid Registration Results on Rigid Registration Non Rigid Registration

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Registration of MR Images

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  1. Registration of MR Images Master Thesis By Naga Padma Krishnam Raju Dandu Supervisor: Ole Fogh Olsen, associate professor, IT University of Copenhagen

  2. Table of Contents • Introduction • Objective • Registration • Rigid Registration • Results on Rigid Registration • Non Rigid Registration • Results on Non Rigid Registration • Conclusion • Future Improvements • References

  3. Introduction • Motivation • to increase the accuracy, while fusing the useful information from differnt MR images, inorder to quatify the articular cartilage of knee during the osteoartherities study • Osteoarthritis Figure 1: (a) Normal knee joint (side view) (b) A knee joint with osteoarthritis [1]

  4. Goals • Quantification • Fusion • Registration

  5. Objective • Registration of MR Image • Why MRI only ?

  6. Definition • Registration • Types of Registration • Type of Image • 2D-2D • 3D-3D • 2D-3D • Subject of image • Intrapersonal • Interpersonal • Modality of Image • Mono Model • Multimodal • Transformation • Rigid • Non Rigid • Search Mode • Landmark • Voxel Intensity

  7. Processes in Registration • Transformation • Interpolation • Similarity Measure + • Optimization Figure : General work flow of registration

  8. Rigid Registration • Rigid Transformation • Translation • Rotation • Interpolation • Nearest Neighbour • Linear Figure : (a) pixel in Template Image (2D) (b) Transformed pixel in template image Figure 12: Finding intensity from boarders using interpolation

  9. Similarity measures-1 • SSD (Sum of the Squared Differences) If is the template image to register and is the reference image then Where : voxel, : transformation and : total number of voxels. • NCC (Normalized Cross Correlation) Here : mean intensity in reference image : mean intensity in template image

  10. Similarity measures-2 • RIU (Ratio Image Uniformity) Here : standard deviation of ratios : mean value of ratios • NMI (Normalized Mutual Information) Here : Marginal Entropy of Image : Joint Entropy of image and

  11. Implementation Details Rigid Registration • Implementing Transformations Figure : (a) pixel in Template Image (2D) (b) Transformed pixel in template image (c)Pixel in transformed template image (d) Reverse transformed pixel co-ordinates in original template image

  12. Implementation Details Rigid Registration • Handling Resolution Differences during Similarity Measure Figure 2: (a) voxel in transformed template image in x y and z-directions (b) voxel in reference image in x, y and z-directions

  13. Implementation Details Rigid Registration • Hierarchal Optimization

  14. Limitations of Rigid Registration Algorithm Rigid Registration • Strictly for rigid features • Not guaranteed under Noisy environment • Local Minimization/Maximization

  15. Results MR Images of Phantom Rigid Registration • Tested on MR Images of Knee and Phantom[7]

  16. Results MR Images of Knee Rigid Registration • Tested on MR Images of Knee and Phantom[7]

  17. Results Phantom MRI Rigid Registration

  18. Results Phantom MRI Rigid Registration

  19. Results Phantom MRI Rigid Registration

  20. Results Knee MRI Rigid Registration

  21. Results Knee MRI Rigid Registration Registered Knee Images with depth level 2 and Sampling rate 0.1 of S.No 1 , 2 and 3 from above table

  22. Results Knee MRI Rigid Registration Registered Knee Images with depth level 2 and Sampling rate 0.05 of S.No 4 , 5, 6 and 7 from above table

  23. Results Knee MRI Rigid Registration Registered Knee Images with depth level 3 and Sampling rate 0.1 of S.No 8 , 9 and 10 from above table

  24. Discussion Rigid Registration • Which Similarity Measure (such as SSD, NCC, RIU and NMI) gave better results? • Why?

  25. Flaws Rigid Registration • Good guess of initial sampling rate is needed • Execution is too slow. Better optimization • Programs are hard coded in parameter re-initialization • Noise tolarability

  26. Non Rigid Registration Non Rigid Registration • Why Non Rigid Registration ? • What is Non Rigid Registration ?

  27. Non Rigid Transformations 1 Non Rigid Registration • Scale Transformations • Affine Transformations

  28. Non Rigid Transformations 2 Non Rigid Registration • Curve Transformations • Cubic Splines • Thin plate splines Figure : Grid of Knots for Cubic Splines

  29. Non Rigid Transformations 3 Non Rigid Registration • Two level Transformation Model • Regularization term* • Cost Function ( similarity measure) *This penalty term was earlier used by [33] D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes for their application to Brest MR Images. The same penalty term is adapted to the current Knee MR Images.

  30. Search Criteria Non Rigid Registration • Search Criteria Figure : search criteria in non-rigid registration

  31. Algorithm Non Rigid Registration • Algorithm • Do the rigid registration and find out optimal translational and rotational parameters • Initialize search region for scale transformations and transform the image and find the optimal scale transformation • Initialize grid step size & depth of hierarchy level • Repeat until required depth is achieved • For each possible position of control voxels grid • Initialize control voxel grid • Perform spline interpolation • Calculate the extra penalty term called regularizar • Calculate the total cost • Update depth & grid step size • Find the minimum of all costs, optimal depth and display corresponding image volume as registered image volume.

  32. Results Knee MRI Non Rigid Registration Registered Knee Images with lamda 0.2 and Depth level 2, 3, 4 and 5

  33. Results Knee MRI Non Rigid Registration Registered Knee Images with lamda 0.2 and Depth level 2, 3, 4 and 5

  34. Discussion Non Rigid Registration • Is the results are improved from rigid registration? • Why? • Any flaws? • Optimal weighting factor? • Is penalty term good enough?

  35. Conclusion 1 Rigid Registration Successfully done • 3D-3D Intrapersonel Multi model registration • Handling of resolution differences between images • Rigid transformations and interpolation using linear interpolation method • Investigation of suitable similarity measure among Sum of the Squared Diffrences, Normalized Cross Correlation, Ratios Image Uniformity, Normalized Mutual Information. • Hirarichal Optimization

  36. Conclusion 2 Rigid Registration Future Improvements • Better Optimization • Better parameter re-initialization • Should handle local minimum/maximum problem • Should include more percentage of voxels during similarity measure • Redundancy should be reduced in iterations • Instead of going from coarse level to finer level during hierarchal optimization, It has to check from small sub image from the center of image to full image verification. • Noise tolerability has to be tested. • Better implementation & executions using Visual c++ ITK tools instead of slow matlab routines

  37. Conclusion 3 Non Rigid Registration Successfully done • Two level transformation approch • Global transformations using Scale transformatins • Local transformations using Cubic Splines • Regulizer term • Similarity mesure using NCC • Improved results Need to be takes care of • Cut off weighing factor Lamda • More experiments to find optimal depth

  38. Conclusion 4 Non Rigid Registration Future Improvements • Instead of checking from the more number of knots to less number of knots in depth levels, it has to verify from less number of knots to more number of knots during depth levels • Noise tolerability has to be tested.

  39. References • Derek L G Hill, Philipp G Batchelor, Mark Holden and David J Hawkes, 12 June 2000, Topical review, Medical Image Registration • J.B. Antoine Maintz, Max A.Viergever, Medical Image Analysis (1998) volume 2, number 1, pp 1-36, Oxford University Press, A Survey of Medical Image Registration. • Rasmus Larsen, DTU, Teaching material for medical image analysis ‘Image registration pixel/voxel based’ • J.Michal Fitzpatrick, Derek L.G.Hill, Calvin R. Maurer. Jr, chapter 8 ’ Image registration’ • A.Ardeshir Goshtasby ‘2-D and 3-D Image registration for Medical, Remote Sensing and Industrial applications’ • John Ashburner & Karl J.Friston, chapter2, ‘Rigid body Registration’ • Phantom and Knee MRI test images from CCBR research institute through Eric Dam • Hongliang Yu, may 2005, Dissertation on ‘automatic Rigid and Deformable Medical image Registration’ • Ramsay & Silverman (1997) “Functional Data Analysis” • Sky McKinley & M Levine “Cubic Spline Interpolation” • Gerardo I. Sánchez-Ortiz, Daniel Rueckert and Peter Burger “Motion and Deformation Analysis of the Heart using Thin-Plate Splines and Density and Velocity Encoded MR Images” http://wwwhomes.doc.ic.ac.uk/~giso/pubs/leedsok/leedsok.html • D. Rueckert,* L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes “Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images” IEEE Transactions on medical imaging, Vol. 18, August 1999 • The Arthritis Research Campaign (arc), http://www.arc.org.uk/about_arth/booklets/6027/6027.htm THANKS

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