Joint reconstruction and segmentation of electron tomography data

Abstract

Scanning transmission electron tomography is often used to reveal the internal structure of material samples. In this technique, a tilt series of tomographic projections is reconstructed to obtain volumetric information. The reconstructed scene is then segmented into regions with homogeneous properties to localize and quantify various material elements. Unfortunately, physical constraints limit the extent of the projection tilt series, leading to artifacts in the reconstructed volume, which can make subsequent segmentation difficult. In this work we use a different, discrete tomography, approach wherein we directly reconstruct only a limited and discrete set of pixel amplitudes, effectively performing the reconstruction and segmentation in a joint fashion. Unlike existing methods, the approach is based on direct formulation of the problem in the discrete domain. Solution of the subsequent challenging optimization problem is achieved through the iterative use of graph-cut methods applied to a physically motivated surrogate cost function. We show reconstruction results using synthetic phantom images for limited angle scenarios and compare them to conventional reconstruction techniques.

Graph-Cut Based Discrete-Valued Image Reconstruction

“…We assume that the label set to be used in the graph-cut based techniques and in the DART technique is known in advance. Joint estimation of these labels and reconstruction are part of on going work for which primary results have been shown in [37].…”

“…The scan that was used in this work contains graphite and aluminum rods in air. We estimated label values for aluminum and graphite using the method explained in [37]. This label estimation technique uses k-means clustering together with morphological filtering to estimate label values.…”