Segmenting Images by Combining Selected Atlases on Manifold
Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.
KeywordsPatient Image Locality Preserve Projection Label Image Manifold Learning Nonlinear Dimensionality Reduction
- 5.Roche, A., Malandain, G., Ayache, N.: Unifying maximum likelihood approaches in medical image registration. Int. J. Imag. Syst. Technol., 71–80 (2000)Google Scholar
- 6.Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290 (2000)Google Scholar
- 7.Roweis, S., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000)Google Scholar
- 8.He, X., Niyogi, P.: Locality preserving projections. In: Proc. Neural Information Processing Systems (2003)Google Scholar
- 9.Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neigbor embedding. In: Proc. IEEE CVPR, vol. 1, pp. 275–282 (2004)Google Scholar