The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images,
due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present:
the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks,
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.
We propose here a supervised classification scheme that does not require any anatomical a priori information to identify
the 3 classes, "GM", "WM", and "background". Features are extracted by means of 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. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2
original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel.
From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2
expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of
99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification
accuracy of 98.79%.