P. Milanfar et al., RECONSTRUCTING BINARY POLYGONAL OBJECTS FROM PROJECTIONS - A STATISTICAL VIEW, CVGIP. Graphical models and image processing, 56(5), 1994, pp. 371-391
Citations number
35
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Software Graphycs Programming
In many applications of tomography, the fundamental quantities of inte
rest in an image are geometric ones. In these instances, pixel-based s
ignal processing and reconstruction is at best inefficient, and, at wo
rst, nonrobust in its use of the available tomographic data. Classical
reconstruction techniques such as filtered back-projection tend to pr
oduce spurious features when data is sparse and noisy; these ''ghosts'
' further complicate the process of extracting what is often a limited
number of rather simple geometric features. In this paper, we present
a framework that, in its most general form, is a statistically optima
l technique for the extraction of specific geometric features of objec
ts directly from the noisy projection data. We focus on the tomographi
c reconstruction of binary polygonal objects from sparse and noisy dat
a. In our setting, the tomographic reconstruction problem is essential
ly formulated as a (finite-dimensional) parameter estimation problem.
In particular, the vertices of binary polygons are used as their defin
ing parameters. Under the assumption that the projection data are corr
upted by Gaussian white noise, we use the maximum likelihood (ML) crit
erion, when the number of parameters is assumed known, and the minimum
description length (MDL) criterion for reconstruction when the number
of parameters is not known. The resulting optimization problems are n
onlinear and thus are plagued by numerous extraneous local extrema, ma
king their solution far from trivial. In particular, proper initializa
tion of any iterative technique is essential for good performance. To
this end, we provide a novel method to construct a reliable yet simple
initial guess for the solution. This procedure is based on the estima
ted moments of the object, which may be conveniently obtained directly
from the noisy projection data. (C) 1994 Academic Press, Inc.