Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

Enea Poletti; Elisa Veronese; Massimiliano Calabrese; Alessandra Bertoldo; Enrico Grisan

doi:10.1117/12.911302

14 February 2012 Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

Enea Poletti, Elisa Veronese, Massimiliano Calabrese, Alessandra Bertoldo, Enrico Grisan

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83142T (2012) https://doi.org/10.1117/12.911302
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

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%.

Citation Download Citation

Enea Poletti, Elisa Veronese, Massimiliano Calabrese, Alessandra Bertoldo, and Enrico Grisan "Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142T (14 February 2012); https://doi.org/10.1117/12.911302

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