We present an optimized supervised segmentation method from multimodal MRIs. As MR images do not behave as natural images, using a spectral gradient based on a psycho-visual paradigm is sub-optimal. Therefore, we propose to create an optimized colorimetric spectral gradient using multi-modalities MRIs. To that purpose, the algorithm learns the optimized parameters of the spectral gradient based on ground truth which are either phantoms or manual delineations of an expert. Using Dice Similarity Coefficient as a cost function for an optimization algorithm, we were able to compute an optimized gradient and to utilize it in order to segment MRIs with the same kind of modalities. Results show that the optimized gradient matrices perform significantly better segmentations and that the supervized learning of an optimized matrix is a good way to enhance the segmentation method.