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.


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.


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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

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