Authors: Carlo Ciulla; Ustijana Rechkoska Shikoska; Dimitar Veljanovski; Filip A. Risteski
Addresses: St. Paul the Apostle, University of Information Science & Technology, Partizanska B.B., Ohrid, 6000, Republic of Macedonia ' St. Paul the Apostle, University of Information Science & Technology, Partizanska B.B., Ohrid, 6000, Republic of Macedonia ' Department of Radiology, General Hospital 8-mi Septemvri, Boulevard 8th September, Skopje, 1000, Macedonia ' Department of Radiology, General Hospital 8-mi Septemvri, Boulevard 8th September, Skopje, 1000, Republic of Macedonia
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.
Keywords: modelling; identification; control; bivariate cubic polynomial model; bivariate cubic Lagrange polynomial model; intensity-curvature; intensity-curvature functional; ICF; highlight; human brain; magnetic resonance imaging; vasculature; inverse Fourier transformation; intensity-curvature term before interpolation; intensity-curvature term after interpolation.
International Journal of Modelling, Identification and Control, 2018 Vol.29 No.3, pp.233 - 243
Available online: 10 Apr 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article