Abstract
In this paper we present a segmentation method for ultrasound (US) images of the pediatric kidney, a difficult and barely studied problem. Our method segments the kidney on 2D sagittal US images and relies on minimal user intervention and a combination of improvements made to the Active Shape Model (ASM) framework. Our contributions include particle swarm initialization and profile training with rotation correction. We also introduce our methodology for segmentation of the kidney’s collecting system (CS), based on graph-cuts (GC) with intensity and positional priors. Our intensity model corrects for intensity bias by comparison with other biased versions of the most similar kidneys in the training set. We prove significant improvements (p < 0.001) with respect to classic ASM and GC for kidney and CS segmentation, respectively. We use our semi-automatic method to compute the hydronephrosis index (HI) with an average error of 2.67±5.22 percentage points similar to the error of manual HI between different operators of 2.31±4.54 percentage points.
Chapter PDF
Similar content being viewed by others
Keywords
- Particle Swarm Optimization
- Probability Density Function
- Training Image
- Kernel Density Estimation
- Active Shape Model
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
Martín-Fernández, M., Alberola-Lopez, C.: An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours. Med. Image. Anal. 9(1), 1–23 (2005)
Xie, J., Jiang, Y., Tsui, H.: Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans. Med. Imag. 24(1), 45–57 (2005)
Noble, J.: Ultrasound image segmentation and tissue characterization. Proc. Inst. Mech. Eng. H J. Eng. Med. 224(2), 307–316 (2010)
Peters, C., Chevalier, R.: Congenital urinary obstruction: Pathophysiology and clinical evaluation. In: Wein, A., Kavoussi, L., Novick, A., Partin, A., Peters, C. (eds.) Campbell-Walsh Textbook of Urology. Elsevier Inc., Philadelphia (2012)
Shapiro, S., Wahl, E., Silberstein, M., Steinhardt, G.: Hydronephrosis index: A new method to track patients with hydronephrosis quantitatively. Urology 72(3), 536–538 (2008)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Xiao, G., Brady, M., Noble, J., Zhang, Y.: Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans. Med. Imag. 21(1), 48–57 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mendoza, C.S. et al. (2013). Automatic Analysis of Pediatric Renal Ultrasound Using Shape, Anatomical and Image Acquisition Priors. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)