Magnetic resonance imaging (MRI) allows the acquisition of high-resolution images of the brain. The diagnosis of various brain illnesses is supported by the distinguished analysis of the different kind of brain tissues, which imply their segmentation and classification. Brain MRI is organized in volumes composed by millions of voxels (at least 65.536 per slice, for at least 50 slices), hence the problem of labeling of brain tissue classes in the composition of atlases and ground truth references, which are needed for the training and the validation of machine-learning methods employed for brain segmentation. We propose a stacking classification scheme that does not require any other anatomical information to identify the 3 classes, gray matter (GM), white matter (WM) and Cerebro- Spinal Fluid (CSF). We employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery (DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone. Features are extracted using a local multi-scale texture analysis, computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation, kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5, and 7x7 pixels. A stacked classifier is proposed exploiting the a priori knowledge about DIR and FLAIR features. Results highlight a significative improvement in classification performance with respect to using all the features in a state-of-the-art single classifier.
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- Stacked Models for Efficient Annotation of Brain Tissues in MR Volumes