Advertisement

Application Of Deformable Models For The Detection Of Acute Renal Rejection

  • Ayman El-Baz
  • Aly A. Farag
  • Seniha E. Yuksel
  • Mohamed E. A. El-Ghar
  • Tarek A. Eldiasty
  • Mohamed A. Ghoneim
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

Acute rejection is the most common reason for graft failure after kidney transplantation, and early detection is crucial to survival of function in the transplanted kidney. In this study we introduce a new framework for automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI). The proposed framework consists of three main steps. The first isolates the kidney from the surrounding anatomical structures by evolving a deformable model based on two density functions; the first function describes the distribution of the gray level inside and outside the kidney region and the second describes the prior shape of the kidney. In the second step, nonrigid registration algorithms are employed to account for the motion of the kidney due to the patient’s breathing. In the third step, the perfusion curves that show transportation of the contrast agent into the tissue are obtained from the segmented cortex of the whole image sequence of the patient. In the final step, we collect four features from these curves and use Bayesian classifiers to distinguish between acute rejection and normal transplants. Applications of the proposed approach yield promising results thatwould, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction.

Keywords

Acute Rejection Color Version Deformable Model Dynamic Contrast Enhance Magnetic Resonance Image Empirical Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    2000 annual report of the U.S. scientific registry of transplant recipients and the organ procure-ment and transplantation network: transplant data 1990-1999. 2001. Richmond, VA: United Network for Organ Sharing, Richmond, VA.Google Scholar
  2. 2.
    Sharma RK, Gupta RK, Poptani H, Pandey CM, Gujral RB, Bhandari M. 1995. The magnetic resonance renogram in renal transplant evaluation using dynamic contrast-enhanced MR imaging. Radiology 59:1405-1409.Google Scholar
  3. 3.
    Kasiske BL, Keane WF. 1996. Laboratory assessment of renal disease: clearance, urinalysis, and renal biopsy. In The kidney, 5th ed., pp. 1137-1173. Ed BM Brenner, FC Rector. Philadelphia: Saunders.Google Scholar
  4. 4.
    Bennett HF, Li D. 1997. MR imaging of renal function. Magn Reson Imaging Clin North Am 5(1):107-126.Google Scholar
  5. 5.
    Giele ELW. 2002. Computer methods for semi-automatic MR renogram determination. PhD dissertation. Department of Electrical Engineering, University of Technology, Eindhoven.Google Scholar
  6. 6.
    Taylor A, Nally JV. 1995. Clinical applications of renal scintigraphy. Am J Roentgenol 164:31-41.Google Scholar
  7. 7.
    Katzberg RW, Buonocore MH, Ivanovic M, Pellot-Barakat C, Ryan RM, Whang K, Brock JM, Jones CD. 2001. Functional, dynamic and anatomic MR urography: feasibility and preliminary findings. Acad Radiol 8:1083-1099.CrossRefGoogle Scholar
  8. 8.
    Tublin ME, Bude RO, Platt JF. 2003. The resistive index in renal Doppler sonography: where do we stand? Am J Roentgenol 180(4):885-892.Google Scholar
  9. 9.
    Huang J, Chow L, Sommer FG, Li KCP. 2001. Power Doppler imaging and resistance index measurement in the evaluation of acute renal transplant rejection. J Clin Ultrasound 29:483-490.CrossRefGoogle Scholar
  10. 10.
    Turetschek K, Nasel C, Wunderbaldinger P, Diem K, Hittmair K, Mostbeck GH. 1996. Power Doppler versus color Doppler imaging in renal allograft evaluation. J Ultrasound Med 15(7):517-522.Google Scholar
  11. 11.
    Trillaud H, Merville P, Tran Le Linh P, Palussiere J, Potaux L, Grenier N. 1998. Color Doppler sonography in early renal transplantation follow-up: resistive index measurements versus power Doppler sonography. Am J Roentgenol 171(6):1611-16115.Google Scholar
  12. 12.
    Yang D, Ye Q, Williams M, Sun Y, Hu TCC, Williams DS, Moura JMF, Ho C. 2001. USPIO enhanced dynamic MRI: evaluation of normal and transplanted rat kidneys. Magn Reson Med 46:1152-1163.CrossRefGoogle Scholar
  13. 13.
    Chan L. 1999. Transplant rejection and its treatment. In Atlas of diseases of the kidney, Vol. 5, chap. 9. Series Ed RW Schrier. Philadelphia: Current Medicine Inc.Google Scholar
  14. 14.
    Szolar DH, Preidler K, Ebner F, Kammerhuber F, Horn S, Ratschek M, Ranner G, Petritsch P, Horina JH. 1997. Functional magnetic resonance imaging of the human renal allografts during the post-transplant period: preliminary observations. Magn Reson Imaging 15(7):727-735.CrossRefGoogle Scholar
  15. 15.
    Lorraine KS, Racusen C. 1999. Acute tubular necrosis in an allograft. Atlas of diseases of the kidney, Vol. 1, chap. 10. Series Ed RW Schrier. Philadelphia: Current Medicine Inc.Google Scholar
  16. 16.
    Krestin GP, Friedmann G, Steinbrich W. 1988. Gd-DTPA enhanced fast dynamic MRI of the kidneys and adrenals. Diagn Imaging Int 4:40-44.Google Scholar
  17. 17.
    Krestin GP, Friedmann G, Steinbrich W. 1988. Quantitative evaluation of renal function with rapid dynamic gadolinium-DTPA enhanced MRI. In Proceedings of the international society for magnetic resonance in medicine, Book of Abstracts. Los Angeles: MRSTS.Google Scholar
  18. 18.
    Frank JA, Choyke PL, Girton M. 1989. Gadolinium-DTPA enhanced dynamic MR imaging in the evaluation of cisplatinum nephrotoxicity. J Comput Assist Tomogr 13:448-459.CrossRefGoogle Scholar
  19. 19.
    Knesplova L, Krestin GP. 1998. Magnetic resonance in the assessment of renal function. Eur Radiol 8:201-211.CrossRefGoogle Scholar
  20. 20.
    Choyke PL, Frank JA, Girton ME, Inscoe SW, Carvlin MJ, Black JL, Austin HA, Dwyer AJ. 1989. Dynamic Gd-DTPA-enhanced MR imaging of the kidney: experimental results. Radiology 170:713-720.Google Scholar
  21. 21.
    Sun Y, Jolly M, Moura JMF. 2004. Integrated registration of dynamic renal perfusion MR im-ages. In Proceedings of the IEEE international conference on image processing, pp. 1923-1926. Washington, DC: IEEEGoogle Scholar
  22. 22.
    Yim PJ, Marcos HB, McAuliffe M, McGarry D, Heaton I, Choyke PL. 2001. Registration of time-series contrast enhanced magnetic resonance images for renography. In Proceedings of the 14th IEEE symposium on computer-based medical systems, pp. 516-520. Washington, DC: IEEE.CrossRefGoogle Scholar
  23. 23.
    Sun Y, Moura JMF, Ho C. 2004. Subpixel registration in renal perfusion MR image sequence. In Proceedings of an IEEE international symposium on biomedical imaging, pp. 700-703, Wash-ington, DC: IEEE.Google Scholar
  24. 24.
    Sun Y, Moura JMF, Yang D, Ye Q, Ho C. 2002. Kidney segmentation in MRI sequences using temporal dynamics. In Proceedings of an IEEE international symposium on biomedical imaging, pp. 98-101. Washington, DC: IEEE.Google Scholar
  25. 25.
    Gerig G, Kikinis R, Kuoni W, van Schulthess GK, Kubler O. 1992. Semiautomated ROI analysis in dynamic MRI studies, part I: image analysis tools for automatic correction of organ displacements. IEEE Trans Image Process 11:(2):221-232.Google Scholar
  26. 26.
    von Schulthess GK, Kuoni W, Gerig G, Duewell S, Krestin G. 1991. Semiautomated ROI analysis in dynamic MRI studies, part II: application to renal function examination, first experiences. J Comput Assist Tomogr 2:733-741.CrossRefGoogle Scholar
  27. 27.
    Liang Z, Lauterbur PC. 1994. An efficient method for dynamic magnetic resonance imaging. IEEE Trans Med Imaging 13(4):677-686.CrossRefGoogle Scholar
  28. 28.
    Giele ELW, de Priester JA, Blom JA, den Boer JA, van Engelshoven JMA, Hasman A, Geerlings M. 2001. Movement correction of the kidney in dynamic MRI scans using FFT phase difference movement detection. J Magn Reson Imaging 14(6):741-749.CrossRefGoogle Scholar
  29. 29.
    Vosshenrich R, Kallerhoff M, Grone HJ, Fischer U, Funke M, Kopka L, Siebert G, Ringert RH, Grabbe E. 1996. Detection of renal ischemic lesions using Gd-DTPA enhanced turbo flash MRI: experimental and clinical results. J Comput Assist Tomogr 20(2):236-243.CrossRefGoogle Scholar
  30. 30.
    Munechika H, Sullivan DC, Hedlund LW, Beam CA, Sostman HD, Herfkens RJ, Pelc NJ. 1991. Evaluation of acute renal failure with magnetic resonance imaging using gradient-echo and Gd-DTPA. Invest Radiol 26(1):22-27.CrossRefGoogle Scholar
  31. 31.
    Carvlin MJ, Arger PH, Kundel HL, Axel L, Dougherty L, Kassab EA, Moore B. 1987. Acute tubular necrosis: use of gadolinium-DTPA and fast MR imaging to evaluate renal function in the rabbit. J Comput Assist Tomogr 11(3):488-95.CrossRefGoogle Scholar
  32. 32.
    Dalla-Palma L, Panzetta G, Pozzi-Mucelli RS, Galli G, Cova M, Meduri S. 2000. Dynamic magnetic resonance imaging in the assessment of chronic medical nephropathies with impaired renal function. Eur Radiol 10(2):280-286.CrossRefGoogle Scholar
  33. 33.
    Kikinis R, von Schulthess GK, Jager P, Durr R, Bino M, Kuoni W, Kubler O. 1987. Normal and hydronephrotic kidney: evaluation of renal function with contrast-enhanced MR imaging. Radiology 165(3):837-842.Google Scholar
  34. 34.
    Semelka RC, Hricak H, Tomei E, Floth A, Stoller M. 1990. Obstructive nephropathy: evaluation with dynamic Gd-DTPA-enhanced MR imaging. Radiology 175:797-803.Google Scholar
  35. 35.
    Beckmann N, Joergensen J, Bruttel K, Rudin M, Schuurman HJ. 1996. Magnetic resonance imaging for the evaluation of rejection of a kidney allograft in the rat. Transpl Int 9(3):175-83.Google Scholar
  36. 36.
    Preidler KW, Szolar D, Schreyer H, Ebner F, Kern R, Holzer H, Horina JH. 1996. Differentiation of delayed kidney graft function with gadolinium-DTPA-enhanced magnetic resonance imaging and Doppler ultrasound. Invest Radiol 31(6):364-371.CrossRefGoogle Scholar
  37. 37.
    El-Diasty T, Mansour O, Farouk A. 2003. Diuretic contrast enhanced mru versus ivu for depiction of non-dilated urinary tract. Abd Imaging 28:135-145.CrossRefGoogle Scholar
  38. 38.
    Laurent D, Poirier K, Wasvary J, Rudin M. 2002. Effect of essential hypertension on kidney function as measured in rat by dynamic MRI. Magn Reson Med 47(1):127-131.CrossRefGoogle Scholar
  39. 39.
    Krestin GP. 1994. Magnetic resonance imaging of the kidneys: current status. Magn Reson Q 10:2-21.Google Scholar
  40. 40.
    Sun Y. 2004. Registration and segmentation in perfusion MRI: kidneys and hearts. PhD disserta-tion, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburg.Google Scholar
  41. 41.
    de Priester JA, Kessels AG, Giele EL, den Boer JA, Christiaans MHL, Hasman A, van Engelshoven JMA. 2001. MR renography by semiautomated image analysis: performance in renal transplant recipients. J Magn Reson Imaging 14(2):134-140.CrossRefGoogle Scholar
  42. 42.
    BoykovY, Lee VS, Rusinek H, Bansal R. 2001. Segmentation of dynamic N-D data sets via graph cuts using Markov models. In Proceedings of the 4th international conference on medical image computing and computer-assisted intervention (MICCAI). Lecture Notes in Computer Science, Vol. 2208, pp. 1058-1066. Utrecht: Springer.Google Scholar
  43. 43.
    Sun Y, Yang D, Ye Q, Williams M, Moura JMF, Boada F, Liang Z, Ho C. 2003. Improving spatiotemporal resolution of USPIO-enhanced dynamic imaging of rat kidneys. Magn Reson Imaging 21:593-598.CrossRefGoogle Scholar
  44. 44.
    Chan TF, Vese LA. 2001. Active contours without edges. IEEE Trans Image Process 10(2):266-277.MATHCrossRefGoogle Scholar
  45. 45.
    Ibanez L, Schroeder W, Ng L, Cates J, and the Insight Software Consortium. 2005. The ITK software guide. Clifton Park, NY: Kitware Inc.Google Scholar
  46. 46.
    Sethian JA. 1996. Level set methods and fast marching methods. Cambridge: Cambridge UP.Google Scholar
  47. 47.
    Caselles V, Kimmel R, Sapiro G. 1997. Geodesic active contours. Int J Comput Vision 22(1):61-79.MATHCrossRefGoogle Scholar
  48. 48.
    Rousson M, Paragios N. 2002. Shape priors for level set representations. In Proceedings of the 7th European conference on computer vision, part II (ECCV’02). Lecture Notes in Computer Science, Vol. 2751, pp. 78-92. Berlin: Springer.Google Scholar
  49. 49.
    Leventon M, Grimson WL, Faugeras O. 2000. Statistical shape influence in geodesic active contours. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1316-1324. Washington, DC: IEEE Computer Society.Google Scholar
  50. 50.
    Chen Y, Thiruvenkadam S, Tagare H, Huang F, Wilson D. 2001. On the incorporation of shape priors into geometric active contours. In IEEE workshop on variational and level set methods, pp. 145-152, Washington, DC: IEEE.CrossRefGoogle Scholar
  51. 51.
    Tsai A, Yezzi AJ, Wells WM, Tempany C, Tucker D, Fan A, Eric W, Grimson L, Willsky AS. 2001. Model-based curve evolution technique for image segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 463-468, Washington, DC: IEEE Computer Society.Google Scholar
  52. 52.
    Paragios N. 2003. A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans Med Imaging 22:773-776.CrossRefGoogle Scholar
  53. 53.
    Litvin A, Karl WC. 2003. Level set-based segmentation using data driven shape prior on feature histograms. In IEEE workshop on statistical signal processing, pp. 166-169. Washington, DC: IEEE.CrossRefGoogle Scholar
  54. 54.
    Tsai A, Wells W, Warfield SK, Willsky AS. 2004. Level set methods in an em framework for shape classification and estimation. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI). Lecture Notes in Computer Science, Vol. 2211, pp. 1-9. Utrecht: Springer.Google Scholar
  55. 55.
    Yang J, Duncan J. 2004. A3d image segmentation of deformable objects with joint shape-intensity prior models using level sets. Med Image Anal 8:285-294.CrossRefGoogle Scholar
  56. 56.
    Yuksel SE, El-Baz A, Shi H, Farag AA, El-Ghar MEA, Eldiasty TA, Ghoneim MA. 2005. Auto-matic detection of renal rejection after kidney transplantation. In Proceedings of the conference on computer assisted radiology and surgery (CARS), pp. 773-778. Berlin: Springer.Google Scholar
  57. 57.
    Witkin A, Kass M, Terzopoulos D. 1987. Snakes: Active contour models. Int J Comput Vision 1:321-331.CrossRefGoogle Scholar
  58. 58.
    El-Baz A, Yuksel SE, Shi H, Farag AA, El-Ghar MA, Eldiasty T, Ghoneim MA. 2005. 2d and 3d shape-based segmentation using deformable models. In Proceedings of the international confer-ence on medical image computing and computer-assisted intervention (MICCAI). Lecture Notes in Computer Science, Vol. 2212,pp. 821-829. Utrecht: Springer.Google Scholar
  59. 59.
    Viola P, Wells WM. 1995. Alignment by maximization of mutual information. In Proceedings of the 5th international conference on computer vision, pp. 16-23. Washington, DC: IEEE Computer Society.CrossRefGoogle Scholar
  60. 60.
    Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson E, Willsky A. 2003. A shape- based approach to curve evolution for segmentation of medical imagery. IEEE Trans Med Imaging 22(2):137-154.CrossRefGoogle Scholar
  61. 61.
    Webb A. 2002. Statistical pattern recognition. 2nd. ed. Chichester: J. Wiley & Sons.MATHGoogle Scholar
  62. 62.
    Schlesinger MI. 1968. A connection between supervised and unsupervised learning in pattern recognition. Kibernetika 2:81-88.Google Scholar
  63. 63.
    Lamperti JW. 1996. Probability. New York: J. Wiley & Sons.MATHGoogle Scholar
  64. 64.
    te Strake L, Kool LJS, Paul LC, Tegzess AM, Weening JJ, Hermans J, Doornbos J, Bluemm RG, Bloem JL. 1988. Magnetic resonance imaging of renal transplants: its value in the differentiation of acute rejection and cyclosporin A nephrotoxicity. Clin Radiol 39(3):220-228.CrossRefGoogle Scholar
  65. 65.
    Farag, AA, El-Baz A, Gimel’farb G. 2004. Density estimation using modified expectation-maximization for a linear combination of gaussians. In Proceding of the IEEE international conference on image processing, Vol. 3, pp. 1871-1874. Washington, DC: IEEE Computer Society.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ayman El-Baz
    • 1
  • Aly A. Farag
    • 1
  • Seniha E. Yuksel
    • 1
  • Mohamed E. A. El-Ghar
    • 2
  • Tarek A. Eldiasty
    • 2
  • Mohamed A. Ghoneim
    • 2
  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisvilleUSA
  2. 2.Urology and Nephrology DepartmentUniversity of MansouraMansouraEgypt

Personalised recommendations