3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function
Kidney segmentation is a key step in developing any non-invasive computer-aided diagnosis (CAD) system for early detection of acute renal rejection. This paper describes a new 3-D segmentation approach for the kidney from computed tomography (CT) images. The kidney borders are segmented from the surrounding abdominal tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated on 21 CT data sets with available manual expert segmentation. The performance evaluation based on the receiver operating characteristic (ROC) and Dice similarity coefficient (DSC) between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed segmentation approach.
KeywordsCompute Tomography Image Segmentation Approach Segmentation Accuracy Volumetric Error Segmentation Framework
- 5.Huang Y.-P, Chung, P.-C., Huang, C.-L., Huang, C.-R.: Multiphase Level Set with Multi Dynamic Shape Models on Kidney Segmentation of CT Image. In: IEEE Biomedical Circuits and Systems Conefernce (BioCas 2009), pp. 141–144 (2009)Google Scholar
- 7.Freiman, M., Kronman, A., Esses, S., Joskowicz, L., Sosna, J.: Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation. In: Jiang, T., Navab, N., Pluim, J., Viergever, M. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 73–80. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 9.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: International Conference on Pattern Recognition (ICPR 2010), pp. 4581–4584 (2010)Google Scholar
- 14.Dice, L.R.: Measures of the Amount of Ecologic Association Between Species. Ecological Society of America 26(3), 297–302 (1945)Google Scholar