Medical Image Analysis
Volume 14, Issue 3 , Pages 255-264 , June 2010

A framework for optimizing measurement weight maps to minimize the required sample size

  • Arish A. Qazi

      Affiliations

    • Department of Computer Science, University of Copenhagen, Denmark
    • ,
  • Dan R. Jørgensen

      Affiliations

    • Department of Computer Science, University of Copenhagen, Denmark
    • Nordic Bioscience Imaging, Herlev Hovedgade, 207 2730 Herlev, Denmark
    • Corresponding Author InformationCorresponding author. Address: Nordic Bioscience Imaging, Herlev Hovedgade, 207 2730 Herlev, Denmark. Tel.: +45 44547767.
    • ,
  • Martin Lillholm

      Affiliations

    • Nordic Bioscience Imaging, Herlev Hovedgade, 207 2730 Herlev, Denmark
    • ,
  • Marco Loog

      Affiliations

    • Pattern Recognition Group, Delft University of Technology, The Netherlands
    • ,
  • Mads Nielsen

      Affiliations

    • Department of Computer Science, University of Copenhagen, Denmark
    • Nordic Bioscience Imaging, Herlev Hovedgade, 207 2730 Herlev, Denmark
    • ,
  • Erik B. Dam

      Affiliations

    • Nordic Bioscience Imaging, Herlev Hovedgade, 207 2730 Herlev, Denmark

Received 11 November 2008 ,Revised 21 January 2010 ,Accepted 21 January 2010.

References 

  1. Andreasen NC, Flashman L, Flaum M, Arndt S, Swayze , Victor I, et al. Regional brain abnormalities in schizophrenia measured with magnetic resonance imaging. JAMA. 1994;272(22):1763–1769
  2. Ashburner J, Friston KJ. Voxel-based morphometry – the methods. Neuroimage. 2000;11(6 Pt 1):805–821June
  3. Baron JC, Chételat G, Desgranges B, Perchey G, Landeau B, de La , et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild alzheimer’s disease. Neuroimage. 2001;14(2):298–309
  4. Biswal S, Hastie T, Andriacchi T, Bergman G, Dillingham M, Lang P. Risk factors for progressive cartilage loss in the knee: a longitudinal magnetic resonance imaging study in forty-three patients. Arthritis and Rheumatism. 2002;46(11):2884–2992
  5. Blumenkrantz G, Lindsey CT, Dunn TCJH, Ries MD, Link TM, Steinbach LS, et al. A pilot, two-year longitudinal study of the interrelationship between trabecular bone and articular cartilage in the osteoarthritic knee. Osteoarthritis and Cartilage. 2004;12:997–1005
  6. Brinkmann BH, Manduca A, Robb RA. Optimized homomorphic unsharp masking for mr grayscale inhomogeneity correction. IEEE Transactions on Medical Imaging. 1998;17(2):161–171
  7. Dam, E.B., Folkesson, J.M., Pettersen, P.C., Christiansen, C., 2006. Automatic cartilage thickness quantification using a statistical shape model. In: MICCAI 2006 Workshop Proceedings: Joint Disease Workshop – Quantitative Automated Musculoskeletal Analysis, pp. 42–49.
  8. Dam EB, Folkesson J, Pettersen P, Christiansen C. Automatic morphometric cartilage quantification in the medial tibial plateau from MRI for osteoarthritis grading. Osteoarthritis and Cartilage. 2007;15:808–818
  9. Dam EB, Fletcher PT, Pizer SM. Automatic shape model building based on principal geodesic analysis bootstrapping. Medical Image Analysis. 2008;12(2):136–151
  10. Dickerson B, Goncharova I, Sullivan M, Forchetti C, Wilson R, Bennett D, et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild alzheimer’s disease. Neurobiology of Aging. 2001;22:747–754(8)
  11. Ding C, Garnero P, Cicuttini F, Scott F, Cooley H, Jones G. Knee cartilage defects: association with early radiographic osteoarthritis, decreased cartilage volume, increased joint surface area and type ii collagen breakdown. Osteoarthritis and Cartilage. 2005;13:198–205
  12. Duda RO, Hart PE, Stork DG. Pattern Classification. second ed.. Wiley-Interscience; 2000;November
  13. Eckstein F, Cicuttini F, Raynauld J, Waterton J, Peterfy C. Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment. Osteoarthritis and Cartilage. 2006;14:46–75
  14. Folkesson J, Dam EB, Olsen OF, Pettersen PC, Christiansen C. Segmenting articular cartilage automatically using a voxel classification approach. IEEE Transactions on Medical Imaging. 2007;26(1):106–115
  15. Freeborough PA, Fox NC. Modeling brain deformations in alzheimer disease by fluid registration of serial 3D MR images. Journal of Computer Assisted Tomography. 1998;22(5):838–843
  16. Good CD, Johnsrude I, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ. Cerebral asymmetry and the effects of sex and handedness on brain structure: A voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage. 2001;14:685–700(16)
  17. Good CD, Scahill RI, Fox NC, Ashburner J, Friston KJ, Chan D, et al. Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias. Neuroimage. 2002;17(1):29–46
  18. Guillemaud R. Uniformity correction with homomorphic filtering on region of interest. International Conference on Image Processing. 1998;2:872–875
  19. Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 2000;42(1):80–86
  20. Hohe J, Faber S, Muehlbauer R, Reiser M, Englmeier KH, Eckstein F. Three-dimensional analysis and visualization of regional MR signal intensity distribution of articular cartilage. Medical Engineering and Physics. 2002;24(3):219–227
  21. Holmes, A., 1994. Statistical issues in functional brain mapping. Ph.D. Thesis, University of Glasgow.
  22. Kelley CT. Iterative methods for optimization. Frontiers in Applied Mathematics. SIAM; 1999;
  23. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Annals of the Rheumatic Diseases. 1957;16:494–501
  24. Kirkwood BR, Sterne JAC. Essential Medical Statistics. second ed.. Blackwell Publishing; 2003;
  25. Lachin JM. Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials. 1981;2(2):93–113
  26. Land, S.R., Friedman, J.H., 1997. Variable fusion: a new adaptive signal regression method. Tech. Rep. 656, Department of Statistics, Carnegie Mellon University Pittsburgh.
  27. Mummery CJ, Patterson K, Price CJ, Ashburner J, Frackowiak RSJ, Hodges JR. A voxel-based morphometry study of semantic dementia: relationship between temporal lobe atrophy and semantic memory. Annals of Neurology. 2000;47:36–45
  28. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping. 2002;15(1):1–25
  29. Nocedal J, Wright SJ. Numerical Optimization. Springer; 1999;
  30. Polak E, Ribière G. Note sur la convergence de méthodes de directions conjuguées. Revue Francaise Informat. Reserche Opérationnelle. 1969;16:35–43
  31. Press WH, Vetterling WT, Teukolsky SA, Flannery BP. Numerical Recipes in C++: the art of scientific computing. New York, NY, USA: Cambridge University Press; 2002;
  32. Pruessner JC, Li LM, Serles W, Pruessner M, Collins DL, Kabani N, et al. Volumetry of Hippocampus and Amygdala with high-resolution MRI and three-dimensional analysis software: minimizing the discrepancies between laboratories. Cerebral Cortex. 2000;10(4):433–442
  33. Qazi A, Folkesson J, Pettersen P, Karsdal M, Christiansen C, Dam E. Separation of healthy and early osteoarthritis by automatic quantification of cartilage homogeneity. Osteoarthritis and Cartilage. 2007;15:1199–1206
  34. Qazi AA, Dam EB, Nielsen M, Karsdal MA, Pettersen PC, Christiansen C. Osteoarthritic cartilage is more homogeneous than healthy cartilage: identification of a superior region of interest colocalized with a major risk factor for osteoarthritis. Academic Radiology. 2007;14:1209–1220
  35. Qazi, A.A., Dam, E.B., Loog, M., Nielsen, M., Lauze, F., Christiansen, C., 2008. A variational method for automatic localization of the most pathological ROI in the knee cartilage. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 6914.
  36. Qvist P, Bay-Jensen AC, Christiansen C, Dam EB, Pastoureau P, Karsdal MA. The disease modifying osteoarthritis drug (DMOAD): is it in the horizon?. Pharmacological Research. 2008;58(1):1–7
  37. Raz N, Gunning-Dixon FM, Head D, Dupuis JH, Acker JD. Neuroanatomical correlates of cognitive aging: evidence from structural magnetic resonance imaging. Neuropsychology. 1998;12(1):95–114
  38. Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D. 1992;60:259–268
  39. Sharma L, Song J, Felson DT, Cahue S, Shamiyeh E, Dunlop DD. The role of knee alignment in disease progression and functional decline in knee osteoarthritis. JAMA. 2001;286(2):188–195
  40. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B. 1996;58:267–288
  41. Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K. Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society: Series B. 2005;67(1):91–108
  42. West M. Bayesian factor regression models in the “large p, small n” paradigm. In: Bayesian Statistics. Oxford University Press; 2003;p. 723–732
  43. Williams, T.G., Taylor, C.J., Gao, Z., Waterton, J.C., 2003. Corresponding articular cartilage thickness measurements in the knee joint by modelling the underlying bone. In: MICCAI (2), pp. 480–487.
  44. Williams, T., Holmes, A., Waterton, J., Maciewicz, R., Nash, A., Taylor, C., 2006. Anatomically equivalent focal regions defined on the bone increases precision when measuring cartilage thickness from knee MRI. In: MICCAI 2006 Workshop Proceedings: Joint Disease Workshop, pp. 25–32.
  45. Wirth W, Eckstein F. A technique for regional analysis of femorotibial cartilage thickness based on quantitative magnetic resonance imaging. IEEE Transactions on Medical Imaging. 2008;27(6):737–744
  46. Wold, H., 1975. Soft modelling by latent variables: the non-linear iterative partial least squares (nipals) approach. In: Perspectives in Probability and Statistics, pp. 117–142.
  47. Xu J, Kobayashi S, Yamaguchi S, Iijima K-i, Okada K, Yamashita K. Gender effects on age-related changes in brain structure. AJNR – American Journal of Neuroradiology. 2000;21(1):112–118
  48. Yushkevich P, Joshi S, Pizer SM. Feature selection for shape-based classification of biological objects. In: Proceedings of Information Processing in Medical Imaging (IPMI). Ambleside; 2002;p. 114–125

PII: S1361-8415(10)00013-7

doi: 10.1016/j.media.2010.01.004

Medical Image Analysis
Volume 14, Issue 3 , Pages 255-264 , June 2010

Article Tools

* Image Usage & Resolution