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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 650463, 7 pages
http://dx.doi.org/10.1155/2013/650463
Research Article

Tracking Lung Tumors in Orthogonal X-Rays

Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA

Received 30 May 2013; Accepted 12 July 2013

Academic Editor: Kayvan Najarian

Copyright © 2013 Feng Li and Fatih Porikli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a computationally very efficient, robust, automatic tracking method that does not require any implanted fiducials for low-contrast tumors. First, it generates a set of motion hypotheses and computes corresponding feature vectors in local windows within orthogonal-axis X-ray images. Then, it fits a regression model that maps features to 3D tumor motions by minimizing geodesic distances on motion manifold. These hypotheses can be jointly generated in 3D to learn a single 3D regression model or in 2D through back projection to learn two 2D models separately. Tumor is tracked by applying regression to the consecutive image pairs while selecting optimal window size at every time. Evaluations are performed on orthogonal X-ray videos of 10 patients. Comparative experimental results demonstrate superior accuracy (~1 pixel average error) and robustness to varying imaging artifacts and noise at the same time.