Automatic segmentation of brain MRI through learning by example

We propose a method for automatic segmentation of brain magnetic resonance images (MRI) using a new approach based on learning. The learning process uses only two images, the original one and its ideal segmented version to generate the decision matrix for each pixel. Reusing the knowledge acquired in the decision matrix carries the segmentation of another similar images. New images are segmented by means of a strategy based on the nearest neighbors, that seeks the best solution in the decision matrix. Performed tests on magnetic resonance nonenhancing images showed promising results in segmenting nonenhancing brain tumors. The main advantages of this method are the facility to faithfully reproduce the objectives of the user, the use of only two images and it does not require the use of heuristic parameters neither the interaction of a specialist user after the learning process.