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Quantitative Analysis of Knee Movement Patterns Through Comparative Visualization

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Visualization in Medicine and Life Sciences III

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

In this paper, we present a novel visualization approach for the quantitative analysis of knee movement patterns in time-varying data sets. The presented approach has been developed for the analysis of patellofemoral instability, which is a common knee problem, caused by the abnormal movement of the patella (kneecap). Manual kinematic parameter calculations across time steps in a dynamic volumetric data set are time-consuming and prone to errors as well as inconsistencies. To overcome these limitations, the proposed approach supports automatic tracking of identified features of interest (FOIs) in the time domain and, thus, facilitates quantitative analysis processes in a semiautomatic manner. Moreover, it allows us to visualize the movement of the patella in the femoral groove during an active flexion and extension movement, which is essential to assess kinematics with respect to knee flexions. To further support quantitative analysis, we propose kinematic plots and time-angle profiles, which enable comparative dynamics visualization. As a result, our proposed visualization approach facilitates better understanding of the effects of surgical interventions by quantifying and comparing the dynamics before and after the operations. We demonstrate our approach using clinical time-varying patellofemoral data, discuss its benefits with respect to quantification as well as medical reporting, and describe how to generalize it to other complex joint movements.

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References

  1. Rhee, S.-J., Pavlou, G., Oakley, J., Barlow, D., Haddad, F.: Modern management of patellar instability. Int. Orthop. 36(12), 2447–2456 (2012)

    Article  Google Scholar 

  2. Desio, S.M., Burks, R.T., Bachus, K.N.: Soft tissue restraints to lateral patellar translation in the human knee. Am. J. Sports Med. 26(1), 59–65 (1998)

    Google Scholar 

  3. Nietosvaara, Y., Aalto, K., Kallio, P.E.: Acute patellar dislocation in children: incidence and associated osteochondral fractures. J. Pediatr. Orthop. 14(4), 513–515 (1994)

    Article  Google Scholar 

  4. Hing, C.B., Smith, T.O., Donell, S.: Surgical versus non-surgical interventions for treating patellar dislocation. Cochrane Database Syst. Rev. 11, (2011). http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD008106.pub2/abstract

  5. Bull, A.M.J., Katchburian, M.V., Shih, Y.-F., Amis, A.A.: Standardisation of the description of patellofemoral motion and comparison between different techniques. Knee Surg. Sports Traumatol. Arthrosc. 10(3), 184–193 (2002)

    Article  Google Scholar 

  6. Christiansen, S.E., Jacobsen, B.W., Lund, B., Lind, M.: Reconstruction of the medial patellofemoral ligament with gracilis tendon autograft in transverse patellar drill holes. Arthrosc. J. Arthrosc. Relat. Surgery 24(1), 82–87 (2008)

    Article  Google Scholar 

  7. Smith, T.O., Davies, L., Toms, A.P., Hing, C.B., Donell, S.T.: The reliability and validity of radiological assessment for patellar instability. A systematic review and meta-analysis. Skelet. Radiol. 40(4), 399–414 (2010)

    Article  Google Scholar 

  8. Davis, D.K., Fithian, D.C.: Techniques of medial retinacular repair and reconstruction. Clin. Orthop. Relat. Res. 402, 38–52 (2002)

    Article  Google Scholar 

  9. Whittle, M.: Gait Analysis: An Introduction. Butterworth-Heinemann Medical, Edinburgh (2007)

    Google Scholar 

  10. Manal, K., Stanhope, S.J.: A novel method for displaying gait and clinical movement analysis data. Gait Posture 20(2), 222–226 (2004)

    Article  Google Scholar 

  11. Manal, K., Chang, C.-C., Hamill, J., Stanhope, S.J.: A three-dimensional data visualization technique for reporting movement pattern deviations. J. Biomech. 38(11), 2151–2156 (2005)

    Article  Google Scholar 

  12. Côté, J.N., Raymond, D., Mathieu, P.A., Feldman, A.G., Levin, M.F.: Differences in multi-joint kinematic patterns of repetitive hammering in healthy, fatigued and shoulder-injured individuals. Clin. Biomech. 20(6), 581–590 (2005)

    Article  Google Scholar 

  13. Keefe, D.F., Ewert, M., Ribarsky, W., Chang, R.: Interactive coordinated multiple-view visualization of biomechanical motion data. IEEE Trans. Vis. Comput. Graph. 15(6), 1383–1390 (2009)

    Article  Google Scholar 

  14. Krekel, P.R., Valstar, E.R., De Groot, J., Post, F.H., Nelissen, R.G.H.H, Botha, C.P.: Visual analysis of multi-joint kinematic data. Comput. Graph. Forum 29(3), 1123–1132 (2010)

    Article  Google Scholar 

  15. Krekel, P.R., Botha, C.P., Valstar, E.R., de Bruin, P.W., Rozing, P.M., Post, F.H.: Interactive simulation and comparative visualisation of the bone-determined range of motion of the human shoulder. In: Proceedings of Simulation and Visualization (SimVis), pp. 275–288 (2006)

    Google Scholar 

  16. Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  17. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  18. Cheung, W., Hamarneh, G.: n-SIFT: n-dimensional scale invariant feature transform. IEEE Trans. Image Process. 18(9), 2012–2021 (2007)

    Google Scholar 

  19. Scovanner, P., Ali, S., Shah, M.: A 3-dimensional SIFT descriptor and its application to action recognition. In: International Conference On Multimedia, pp. 357–360 (2007)

    Google Scholar 

  20. Toews, M., Wells III, W.M.: Efficient and robust model-to-image alignment using 3D scale-invariant features. Med. Image Anal. 17(3), 271–82 (2012)

    Article  Google Scholar 

  21. Ni, D., Qu, Y., Yang, X., Chui, Y.P., Wong, T.-T., Ho, S.S., Heng, P.A.: Volumetric ultrasound panorama based on 3D SIFT. In: Conference on Medical Image Computing and Computer-Assisted Intervention, Part II, pp. 52–60 (2008)

    Google Scholar 

  22. Flitton, G., Breckon, T., Bouallagu, N.M.: Object recognition using 3D SIFT in complex CT volumes. In: British Machine Vision Conference, pp. 11.1–12 (2010)

    Google Scholar 

  23. Flitton, G., Breckon, T.P., Megherbi, N.: A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery. Pattern Recogn. 46(9), 2420–2436 (2013)

    Article  Google Scholar 

  24. Yu, T.-H., Woodford, O.J., Cipolla, R.: A performance evaluation of volumetric 3D interest point detectors. Int. J. Comput. Vis. 102(1–3), 180–197 (2012)

    Google Scholar 

  25. Nguyen, K.T., Ropinski, T.: Feature tracking in time-varying volumetric data through scale invariant feature transform. In: SIGRAD Conference on Visual Computing, pp. 11–16 (2013)

    Google Scholar 

  26. Pronost, N., Sandholm, A., Thalmann, D.: A visualization framework for the analysis of neuromuscular simulations. Vis. Comput. Int. J. Comput. Graph. 27(2), 109–119 (2011)

    Google Scholar 

  27. Lindeberg, T.: Scale-space theory: a basic tool for analysing structures at different scales. J. Appl. Stat. 21, 225–270 (1994)

    Article  Google Scholar 

  28. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1–2), 43–72 (2005)

    Article  Google Scholar 

  29. Allaire, S., Kim, J.J., Breen, S.L., Jaffray, D.A., Pekar, V.: Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2008)

    Google Scholar 

  30. Paganelli, C., Peroni, M., Pennati, F., Baroni, G., Summers, P.: Scale invariant feature transform as feature tracking method in 4D imaging: a feasibility study. In: IEEE Conference on Engineering in Medicine and Biology Society (EMBC), pp. 6543–6546 (2012)

    Google Scholar 

  31. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  32. Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1000–1006 (1997)

    Google Scholar 

  33. Brossmann, J., Muhle, C., Schröder, C., Melchert, U.H., Büll, C.C., Spielmann, R.P., Heller, M.: Patellar tracking patterns during active and passive knee extension: evaluation with motion-triggered cine MR imaging. Radiother. Oncol. 187, 205–212 (1993)

    Google Scholar 

  34. Powers, C.M., Shellock, F.G., Pfaff, M.: Quantification of patellar tracking using kinematic MRI. J. Magn. Reson. Imaging 8(3), 724–732 (1998)

    Article  Google Scholar 

  35. Shellock, F.G., Mink, J.H., Deutsch, A.L., Foo, T.K., Sullenberger, P.: Patellofemoral joint: identification of abnormalities with active-movement, “unloaded” versus “loaded” kinematic MR imaging techniques. Radiother. Oncol. 188, 575–578 (1993)

    Google Scholar 

  36. König, A.H., Doleisch, H., Gröller, E.: Multiple views and magic mirrors - fMRI visualization of the human brain. Technical report, Institute of Computer Graphics and Algorithms, Vienna University of Technology (1999)

    Google Scholar 

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Correspondence to Khoa Tan Nguyen .

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Nguyen, K.T., Gauffin, H., Ynnerman, A., Ropinski, T. (2016). Quantitative Analysis of Knee Movement Patterns Through Comparative Visualization. In: Linsen, L., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences III. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24523-2_12

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