Proceedings Article | 14 February 2012
KEYWORDS: Tissues, Image segmentation, Brain, Magnetic resonance imaging, Image classification, Neuroimaging, Feature extraction, Spatial resolution, Magnetism, Image registration
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%.
