Brain Tumor Segmentation from Multi-Spectral Images Using Multi-Kernel Learning

Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan


We propose a brain tumor segmentation method from multi-spectral MRI images. The new idea is to use Multiple Kernel Learnin (MKL) which jointly selects one or more kernels associated to each feature and trains SVM (Support Vector Machine).

First, a large set of features based on wavelet decomposition is computed on a small number of voxels for all types of images, allowing us to build a training feature base. The segmentation task is then viewed as a learning problem where only the most significant features from the feature base are selected and a classifier is then learned. The images are then segmented using the trained classifier on the selected features.

Our algorithm was tested on the real data provided by the challenge of Brats 2012. The dataset includes 20 high-grade glioma patients and 10 low-grade glioma patients. For each patient, T1, T2, FLAIR, and post-Gadolinium T1 MR images are available. Our algorithm was compared to the resulting top methods of the challenge of Brats 2012. The results show good performances of our method.

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