The detection, segmentation and quantification of multiple sclerosis (MS) lesions on magnetic resonance images (MRI) has been a very active field for the last two decades because of the urge to correlate these measures with the effectiveness of pharmacological treatment. A myriad of methods has been developed and most of these are non specific for the type of lesions and segment the lesions in their acute and chronic phases together. On the other hand, radiologists are able to distinguish between several stages of the disease on different types of MRI images. The main motivation of the work presented here is to computationally emulate the visual perception of the radiologist by using modeling principles of the neuronal centers along the visual system. By using this approach we are able to detect the lesions in the majority of the images in our population sample. This type of approach also allows us to study and improve the analysis of brain networks by introducing a priori information.