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Combined Image Processing Techniques for Characterization of MRI Cartilage of the Knee

Combined Image Processing Techniques for Characterization of MRI Cartilage of the Knee. Author : J. Carballido-Gamio J.S. Bauer Keh-YangLee S. Krause S. Majumdar Source : 27th Annual International Conference of the IEEE-EMBS 2005; On page(s): 3043-3046 Speaker : Ren-Li Shen

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Combined Image Processing Techniques for Characterization of MRI Cartilage of the Knee

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  1. Combined Image Processing Techniques for Characterization of MRI Cartilage of the Knee Author :J. Carballido-GamioJ.S. BauerKeh-YangLee S. KrauseS. Majumdar Source :27th Annual International Conference of the IEEE-EMBS 2005; On page(s): 3043-3046 Speaker : Ren-Li Shen Advisor : Ku-Yaw Chang

  2. Outline • Introduction • Methodology • Results • Discussion • Conclusion

  3. Introduction • Articular cartilage • Common manifestation of osteoarthritis (OA) • Morphological degeneration • Magnetic resonance imaging (MRI) • Visualize and analyze • Purpose • Development new image processing techniques • For quantitative analysis

  4. Introduction • Image process consists • MRI acquisition • Cartilage segmentation • Automatic • Semi-automatic • Interactive • Interpolation • Morphing technique • Registration • 3D shape-contexts • Quantification • Minimum 3D Euclidean distances 3D shape-contexts • Visualization

  5. Outline • Introduction • Methodology • Results • Discussion • Conclusion

  6. Methodology • 17 porcine knees • Sagittal 3D SPGR MR images（Intra-subject） • Knee of 6 subjects（resolution：0.234 mm x 0.234 mm, slice thickness：2mm,obtained at 1.5T） • Sagittal fast spin-echo(FSE) images（Inter-subject） • Both knees of 6 different subjects（resolution：0.3125 mm x 0.3125mm, slice thickness：1.5 mm,obtained at 1.5T）

  7. Methodology

  8. Methodology-Cartilage segmentation • Semi-automatic segmentation technique • Based on edge detection and Bezier splines • Control points • Placed inside the cartilage • Following its shape to create Bezier spline • Smoothing techniques • Anisotropic diffusion • Median filtering • Multiplication

  9. Methodology-Thickness and volume • Compute 3D cartilage thickness • Labeling bone-cartilage and articular • Automatically • For each point on the articular surface • Can find point on the bone-cartilage interface • Corresponding distance was assigned • Thickness value

  10. Methodology-Image registration • Bones to be registered • Using 3D shape-contexts • Robust contour matching • Manual and automatic • Using minimum Euclidean distances • Avoid false matching

  11. Outline • Introduction • Methodology • Results • Discussion • Conclusion

  12. Results • Using Bland-Altman method • Check for any deviation • All methods • Good agreement on all data samples

  13. Outline • Introduction • Methodology • Results • Discussion • Conclusion

  14. Discussion • Proper quantification of thickness and volume • Important to OA of the knee • In follow-up studies • Compare common regions • Have presented an validated • Automatic registration technique

  15. Outline • Introduction • Methodology • Results • Discussion • Conclusion

  16. Conclusion • It’s important to assess the quantification • Knee cartilage morphology • Monitor the progression of joint diseases • Have presented and validated • Accurate image processing tools

  17. The end~

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