Abstract
A promising approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is proposed. The proposed approach consists of three main steps. The first step segments the kidney from the surrounding abdominal tissues by a level-set based deformable model with a speed function that accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. In the second step, to handle local object deformations due to kidney motion caused by patient breathing, we proposed a new nonrigid approach to align the object by solving Laplace’s equation between closed equispaced contours (iso-contours) of the reference and target objects. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the segmented kidneys and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, 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.
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Keywords
- Acute Rejection
- Deformable Model
- Dynamic Contrast Enhance Magnetic Resonance Image
- Nonrigid Registration
- Pixel Pair
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.
References
U.S. Department of Health and Human Services. Annual report of the U.S. scientific registry of transplant recipients and the organ procurement and transplantation network: transplant data: 1990-1999. Bureau of Health Resources Department, Richmond, VA (2000)
Neimatallah, M., et al.: Magnetic resonance imaging in renal transplantation. J. Magn. Reson. Imaging 10(3), 357–368 (1999)
Gerig, G., et al.: 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 (1992)
Giele, E.: Computer methods for semi-automatic MR renogram determination. Ph.D. dissertation, Dept. Elec. Eng., Eindhoven Univ. of Techno., Eindhoven (2002)
Boykov, Y., et al.: Segmentation of dynamic N-D data sets via graph cuts using Markov models. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 1058–1066. Springer, Heidelberg (2001)
Sun, Y., et al.: Integrated registration of dynamic renal perfusion MR images. In: IEEE Int. Conf. Image Process. (ICIP 2004), Singapore, pp. 1923–1926 (October 2004)
Han, X., Xu, C., Prince, J.L.: A Topology preserving level set method for geometric deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 61–79 (2009)
Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Applied Mathematical Sciences, vol. 153. Springer, Heidelberg (2006)
Carson, C., Belongie, S., Greenspan, H., Blobworld, J.M.: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. and Mach. Intell. 24(8), 1026–1038 (2002)
Ma, W.Y., Manjunath, B.S.: Edgeflow technique for boundary detection and image segmentation. IEEE Trans. Image Process. 9(8), 1375–1388 (2000)
Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)
Chen, K., et al.: Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vision 50(3), 315–328 (2002)
Khalifa, F., El-Baz, A., Gimel’farb, G., Ousephand, R., Abu El-Ghar, M.: Shape-Appearance guided level-set deformable model for image segmentation. In: IEEE Int. Conf. Pattern Recog. (ICPR 2010), Istanbul, Turkey (August 2010) (Currently in Press)
El-Baz, A., Gimel’farb, G.: EM Based approximation of empirical distributions with linear combinations of discrete Gaussians. In: Proc. IEEE Int. Conf. Image Process. (ICIP 2007), San Antonio, Texas, USA, September 2007, vol. 4, pp. 373–376 (2007)
Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vision 24(2), 137–154 (1997)
Rueckert, D., et al.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imag. 18(8), 712–721 (1999)
Studholme, C., Constable, R.T., Duncan, J.: Accurate alignment of functional EPI data toanatomical MRI using a physics-based distortion model. IEEE Trans. Med. Imag. 19(11), 1115–1127 (2000)
Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. NeuroImage 23(1), S139–S150 (2004)
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Khalifa, F., El-Baz, A., Gimel’farb, G., El-Ghar, M.A. (2010). Non-invasive Image-Based Approach for Early Detection of Acute Renal Rejection. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_2
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DOI: https://doi.org/10.1007/978-3-642-15705-9_2
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