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A comparison between adaptive kernel density estimation and Gaussian Mixture Regression for real-time tumour motion prediction from external surface motion

Tahavori, F, Alnowami, M and Wells, K (2012) A comparison between adaptive kernel density estimation and Gaussian Mixture Regression for real-time tumour motion prediction from external surface motion IEEE Nuclear Science Symposium Conference Record. pp. 3902-3905.

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Abstract

In this present study, tumour (3D) locations are predicted via external surface motion, extracted from abdomen/ thoracic surface measurements that can be used to enhance dose targeting in external beam radiotherapy. Canonical Correlation Analysis (CCA) is applied to the surface and tumour motion data to maximise the correlation between them. This correlation is exploited for motion prediction [1]. Nine dynamic CT datasets were used to extract the surface and tumour motion and to create the Canonical Correlation model (CCM). Gaussian Mixture Regression (GMR) and Adaptive Kernel Density Estimation (AKDE) were trained on these nine datasets to predict the respiratory signal by updating the surface motion and CCM. A leave-one-out method was used to evaluate and compare the performance of GMR and AKDE in predicting the tumour motion. © 2012 IEEE.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Tahavori, FUNSPECIFIEDUNSPECIFIED
Alnowami, MUNSPECIFIEDUNSPECIFIED
Wells, KUNSPECIFIEDUNSPECIFIED
Date : 27 October 2012
Identification Number : 10.1109/NSSMIC.2012.6551895
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Additional Information : © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 01 Oct 2013 09:44
Last Modified : 09 Jun 2014 13:11
URI: http://epubs.surrey.ac.uk/id/eprint/802414

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