This paper presents a novel and unreported approach developed to filter T2-weighetd Magnetic Resonance Imaging (MRI). The MRI data is fitted with a parametric bivariate cubic Lagrange polynomial, which is used as the model function to build the continuum into the discrete samples of the two-dimensional MRI images. On the basis of the aforementioned model function, the Classic-Curvature (CC) and the Signal Resilient to Interpolation (SRI) images are calculated and they are used as filter masks to convolve the two-dimensional MRI images of the pathological human brain. The pathologies are human brain tumors. The result of the convolution provides with filtered T2-weighted MRI images. It is found that filtering with the CC and the SRI provides with reliable and faithful reproduction of the human brain tumors. The validity of filtering the T2-weighted MRI for the quest of supplemental information about the tumors is also found positive.

This paper presents the calculation of the classic-curvature and the intensity-curvature term before interpolation of a bivariate polynomial model function. The classic-curvature is termed as yc (x, y) and the intensity-curvature term before interpolation is termed as E0. The classic-curvature is defined as the sum of the four second order partial derivatives of the bivariate polynomial. The intensity-curvature term before interpolation is defined as the integral of the product between the pixel intensity value termed as f(0, 0) and the classic-curvature calculated at the origin of the coordinate system of the pixel. This paper presents an application of the calculation of classic-curvature and the intensity-curvature term before interpolation using two-dimensional Magnetic Resonance Imaging (MRI) data and reports for the first time in the literature on the behavior of the intensity-curvature term before interpolation.