The process of transforming the non-linear magnetic field perturbations induced by radiowaves into linear reconstructions based on Radon and Fourier transforms has resulted in MR acquisitions in which intensities do not have a fixed meaning, not even within the same protocol, for the same body region, for images obtained on the same scanner, for the same patient, on the same day. This makes robust image interpretation and processing extremely challenging. The status quo of fine tuning an image processing algorithm with the ever-varying MRI intensity space could best be summarized as a "random search through the parameter space". This work demonstrates the implications of standardizing the contrast across multiple tissue types on the robustness and efficiency of image processing algorithms. Contrast standardization is performed using a prior-knowledge driven feature-guided, fast, non-linear equalization technique. Without loss of generality, skull stripping and brain tissue segmentation are considered in this investigation. Results show that the iterative image processing algorithms converge faster with minimal parameter tweaking and the abstractions are significantly better in the contrast standardized space than in the native stochastic space.