Semi-supervised Learning of Edge Filters for Volumetric Image Segmentation
For every segmentation task, prior knowledge about the object that shall be segmented has to be incorporated. This is typically performed either automatically by using labeled data to train the used algorithm, or by manual adaptation of the algorithm to the specific application. For the segmentation of 3D data, the generation of training sets is very tedious and time consuming, since in most cases, an expert has to mark the object boundaries in all slices of the 3D volume. To avoid this, we developed a new framework that combines unsupervised and supervised learning. First, the possible edge appearances are grouped, such that, in the second step, the expert only has to choose between relevant and non-relevant clusters. This way, even objects with very different edge appearances in different regions of the boundary can be segmented, while the user interaction is limited to a very simple operation. In the presented work, the chosen edge clusters are used to generate a filter for all relevant edges. The filter response is used to generate an edge map based on which an active surface segmentation is performed. The evaluation on the segmentation of plant cells recorded with 3D confocal microscopy yields convincing results.
KeywordsActive Surface Active Contour Object Boundary Spherical Harmonic Expansion Edge Filter
Unable to display preview. Download preview PDF.
- 1.Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. on PAMI 23/6, 681–685 (2001)Google Scholar
- 3.Yushkevich, P.A., Piven, J., Hazlett, C., Smith, H., Smith, G., Ho, R., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31/3 (2006)Google Scholar
- 4.Schulz, J., Schmidt, T., Ronneberger, O., Burkhardt, H., Pasternak, T., Dovzhenko, A., Palme, K.: Fast scalar and vectorial grayscale based invariant features for 3d cell nuclei localization and classification. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 182–191. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 7.Keuper, M., Padeken, J., Heun, P., Burkhardt, H., Ronneberger, O.: A 3d active surface model for the accurate segmentation of drosophila schneider cell nuclei and nucleoli. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 865–874. Springer, Heidelberg (2009)Google Scholar
- 9.Ballard, D.H., Brown, C.M.: Computer vision. Prentice-Hall, NJ (1981)Google Scholar
- 11.Aguet, F., Jacob, M., Unser, M.: Three-dimensional feature detection using optimal steerable filters. In: Proc. of the ICIP, pp. 1158–1161 (2005)Google Scholar