Theoretical analysis of a multiscale algorithm for the direct segmentation of tomographic images

Several multiscale objective functions for the direct segmentation of tomographic images are presented. Standard methods of signal detection and estimation are used to develop a theoretical performance analysis, which quantitatively predicts the performance at realistic noise levels. The analysis compares the relative merit of multiscale and monoscale segmentation, and shows the impact of the Shepp-Logan skull's quantization error.<
>