UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

A combined reconstruction-classification method for diffuse optical tomography

Hiltunen, P; Prince, SJD; Arridge, S; (2009) A combined reconstruction-classification method for diffuse optical tomography. PHYS MED BIOL , 54 (21) 6457 - 6476. 10.1088/0031-9155/54/21/002.

Full text not available from this repository.

Abstract

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

Type:Article
Title:A combined reconstruction-classification method for diffuse optical tomography
DOI:10.1088/0031-9155/54/21/002
Keywords:PIECEWISE-CONSTANT COEFFICIENTS, FINITE-ELEMENT-METHOD, IMAGE-RECONSTRUCTION, REGION BOUNDARIES, SCATTERING MEDIA, REGULARIZATION, MODELS, SHAPE, SEGMENTATION, INFORMATION
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

Archive Staff Only: edit this record