Sequential Projection Selection Methods for Binary Tomography

Abstract

Binary tomography reconstructs binary images from a low number of their projections. Often, there is a freedom how these projections can be chosen which can significantly affect the quality of reconstructions. We apply sequential feature selection methods to find the ‘most informative’ projection set based on a blueprint image. Using various software phantom images, we show that these methods outperform the previously published projection selection algorithms.