The nature of statistical learning theory
The nature of statistical learning theory
Invariant Features for Gray Scale Images
Mustererkennung 1995, 17. DAGM-Symposium
Voxel-wise gray scale invariants for simultaneous segmentation and classification
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Harmonic Filters for Generic Feature Detection in 3D
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Invariance via group-integration: a feature framework for 3D biomedical image analysis
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Phase based 3d texture features
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Voxel-wise gray scale invariants for simultaneous segmentation and classification
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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We introduce and discuss a new method for segmentation and classification of cells from 3D tissue probes. The anisotropic 3D volumetric data of fluorescent marked cell nuclei is recorded by a confocal laser scanning microscope (LSM). Voxel-wise gray scale features (see accompaning paper [1][2]) ), invariant towards 3D rotation of its neighborhood, are extracted from the original data by integrating over the 3D rotation group with non-linear kernels. In an interactive process, support-vector machine models are trained for each cell type using user relevance feedback. With this reference database at hand, segmentation and classification can be achieved in one step, simply by classifying each voxel and performing a connected component labelling, automatically without further human interaction. This general approach easily allows adoption of other cell types or tissue structures just by adding new training samples and re-training the model. Experiments with datasets from chicken chorioallantoic membrane show encouraging results.