We have commonly employed medical images, not including X-ray photography, which have their image enhanced by taking advantage of certain characteristics of the human body to look for some valuable medical information. In general, the medical imaging modalities have employed an image acquisition method which enhances a certain feature of the seat of a disease for later processing of the acquired images. Typical examples of the feature enhancement for medical images are the active area mapping on fMRI using Statistical Parametric Mapping (SPM), and the diffusivity mapping on Diffusion Tensor Imaging (DTI) using Fractional Anisotropy (FA), Apparent Diffusion Coefficient (ADC) or Relative Anisotropy (RA). Especially in DTI, many researchers have been trying to reveal the current state of a disease without any invasion of the body by using some variable discriminants. In this paper, we propose a framework, which supports and promotes the creation of new discriminants for DTI. The proposed system enables the users to create new discriminants by using eigen values from the voxels of DTI data, and to search for important clinical information applying discriminant mapping to the DTI slice images.