title: A curve evolution approach to object-based tomographic reconstruction.
abstract: In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. Instead of reconstructing pixel values on a fixed rectangular grid, we then find a reconstruction by jointly estimating these unknown contours and texture coefficients of the object and background. By designing a new "tomographic flow", the resulting problem is recast into a curve evolution problem and an efficient algorithm based on level set techniques is developed. The performance of the curve evolution method is demonstrated using examples with noisy limited-view Radon transformed data and noisy ground-penetrating radar data. The reconstruction results and computational cost are compared with those of conventional, pixel-based regularization methods. The results indicate that the curve evolution methods achieve improved shape reconstruction and have potential computation and memory advantages over conventional regularized inversion methods.
authors.affiliation: MathWorks Inc., Natick, MA 01760, USA. hfeng@mathworks.com
authors.forename: Haihua
authors.initials: H
authors.lastname: Feng
authors.forename: William Clem
authors.initials: WC
authors.lastname: Karl
authors.forename: David A
authors.initials: DA
authors.lastname: Castañon