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Respiratory motion prediction from CBCT image observations using UKF

Sundarapandian, Manivannan and Kalpathi, Ramakrishnan and Siochi, Alfredo R (2015) Respiratory motion prediction from CBCT image observations using UKF. In: 14th IAPR International Conference on Machine Vision Applications (MVA), MAY 18-22, 2015, Tokyo, JAPAN, pp. 559-562.

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Official URL: http://dx.doi.org/10.1109/MVA.2015.7153254

Abstract

In this paper, we propose a prediction model for breathing pattern based on observations from CBCT raw projection images. From the raw CBCT projections the diaphragm apex position is measured, which in turn is used for the state estimation. We use a novel state space model followed by an Unscented Kalman Filter (UKF). Our method is compared with one of the successful models called Local Circular Motion (LCM). The initial results show that, our model outperforms the LCM model in terms of prediction accuracy.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 08 Oct 2016 06:49
Last Modified: 08 Oct 2016 06:49
URI: http://eprints.iisc.ac.in/id/eprint/54732

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