Grey and white matter recognition in brain image segmentation using multilayer perceptron and superpixels.

The correct segmentation of brain tissues in magnetic resonances images helps the development of higher accuracy diagnoses. A model that allows segmentation soft tissue in brain magnetic resonance images is proposed, where are discriminated between grey matter, white matter and background. To segment images, the single linear iterative clustering of superpixels algorithm was used, as well as the classification between the soft matters of the images was carried out with an artificial neural network of the multilayer perceptron type. To measure the quality of the classification in the segmentation, the Sorensen-Dice coefficient was used, as well as the results of the proposed methodology was compared against the k-means algorithm. The proposed methodology reached a Sorensen-Dice coefficient of 0.921 and 0.963, for gray and white matter, respectively, for images with 0% noise, while the k-means only reached 0.911 and 0.958 under the same conditions. In images with 9% noise, the difference was even greater, with a 0.863 and 0.937 classification between grey and white matter, respectively, while for the k-means algorithm it decreased to 0.805 and 0.901, respectively. When the image is dividing in superpixels the structure of the brain regions is better preserved than other methodologies as k- means. Therefore, the neural network works individually in the classification with each superpixel. Where a multilayer perceptron network can be used as accuracy classifier of brain tissue in magnetic resonance imaging was proven. [ABSTRACT FROM AUTHOR]