A Parametric Level-Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual-Energy Computed Tomography

Abstract

Dual energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual energy processing algorithm with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown, cluttered background. Physical properties of the objects, specifically the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object as well as fundamental, physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and thus can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods which define the level set function non-parametrically, i.e., as a dense set of pixel values), we define our level set parametrically via radial basis functions (RBF's) and employ a Gauss-Newton type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives a adequate reconstruction of the background.

A review of automated image understanding within 3D baggage computed tomography security screening

“…This is most likely due to their susceptibility to artefacts in the reconstructed images [88]. Only very recently has the Dual-Energy Index (DEI) [48] (a post-reconstruction DECT metric that offers a crude estimate of the effective atomic number of a material) been used for the segmentation of 3D baggage-CT imagery (see Tables 3 and 4).…”

“…Although the majority of dual-energy decomposition algorithms have been motivated by medical applications, there has been a growing interest in the application of similar techniques for explosives detection in baggage-screening systems [111,78,88]. The fundamental objectives of dual-energy decomposition in the medical and security-screening domains differ.…”

“…Semerci and Miller [88] present a polychromatic DECT algorithm, tailored particularly for the detection of objects in cluttered baggage-CT images. The availability of a priori information regarding the Compton scatter and photoelectric absorption coefficients of the objects of interest is assumed.…”

“…Several more robust DECT-based techniques exist for computing the effective atomic number [111,93,88]. The majority of these rely on the decomposition of the low and highenergy data into equivalent Compton scatter and photoelectric absorption coefficients using the methods described in Section 4.2.…”

Materials-based 3D segmentation of unknown objects from dual-energy computed tomography imagery in baggage security screening

“…Prereconstruction [27] and iterative reconstruction [28] techniques are generally concerned with solving the material decomposition problem and subsequently determining the effective atomic number and density images. While a variety of DECT-decomposition techniques exist (e.g.…”