Intensity-curvature highlight of human brain magnetic resonance imaging vasculature

Abstract

This paper uses the concept of intensity-curvature to highlight human brain vasculature imaged through magnetic resonance imaging (MRI). Two model functions are fitted to the MRI data. The model functions are: 1) the bivariate cubic polynomial (B32D), 2) the bivariate cubic Lagrange polynomial (G42D). The concept of intensity-curvature entails the calculation of the classic-curvature and the two intensity-curvature terms (ICTs): before and after interpolation. When the two intensity-curvature terms are calculated on a pixel-by-pixel basis across the image, they become two additional images. Through the use of the aforementioned ICT images it is possible to highlight and filter the human brain vasculature imaged with MRI. Moreover, the inverse Fourier transformation of the difference between the k-space of the MRI and the k-space of the ICT provides vessels identification. In essence, this research presents evidence that MRI images of the human brain can be studied through two additional domains: the intensity-curvature terms.