Technical Report Details
| Date |
23-4-2008 |
| Number |
DISI-TR-08-10 |
| Title |
Multi-Cues Regularized Least-Squares applied to Brain MRI Segmentation |
| Authors |
C. Basso, E. De Vito, A. Verri |
| Bibtex Entry |
@TECHREPORT{basso08tr,
author = {Basso, Curzio and De Vito, Ernesto and Verri, Alessandro},
ti |
| E-mail |
curzio.basso@disi.unige.it |
| Link |
http://www.disi.unige.it/person/BassoC/pubs/basso08tr.pdf |
| Abstract |
We present a method for using multiple image cues in a Regularized Least-Squares (RLS) regression scheme. The cues are generic continuous functions defined on the object
space, such as the raw intensities or the gradient; their combinations with a Mercer kernel yield a set of cue-specific kernels that we use for regression and classification. The regression problem is cast in the direct sum space of the Reproducing Kernel Hilbert Spaces (RKHS) associated to each cue-specific kernel. This particular formulation of the problem is consistent, and can be solved by iterative or closed-form methods. Setting the problem in the direct sum space allows us to design a feature selection mechanism which operates independently on the training points and on the cue types. We show an implementation of the selection stage based on a consistent algorithm that minimizes the well known elastic-net functional. The method is applied to the automated segmentation of 3D magnetic resonance images (MRI) of the brain, approached as a voxel classification problem. The actual segmentation is performed via a number of one-versus-all least-squares classifiers, trained solving a multi-cue RLS regression problem and combined via bagging. The tests are performed on a set of publicly available, simulated T1 MRIs. |
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