Hilde Segers1,
Willem Jan Palenstijn1, Kees Joost Batenburg, 12, Jan
Sijbers1
1Vision
Lab - dept. Physics, Universiteit Antwerpen, Antwerpen, Belgium; 2Center
for Mathematics and Informatics, Amsterdam, Noord-Holland, Netherlands
Segmentation refers to the classification of image pixels into distinct classes, typically based on their grey level. It is usually performed as a post-processing step on an MR magnitude image, which is influenced by reconstruction artifacts. In this abstract, we investigate the integration of reconstruction and segmentation into one single procedure. This combination is a regularized reconstruction problem where we exploit prior knowledge about the discreteness of the grey levels. Simulation results show that this integrated method yields better results than the conventional approach when the underlying truth is a discrete image.