2024-03-29T06:20:17Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1589462023-05-03T09:45:24Zcom_10234_43662com_10234_9col_10234_43643
Repositori UJI
author
Vlad, Iulian T.
author
Juan, Pablo
author
Mateu, Jorge
2016-04-25T18:09:27Z
2016-04-25T18:09:27Z
2015-09
0898-1221
http://hdl.handle.net/10234/158946
http://dx.doi.org/10.1016/j.camwa.2015.06.006
In this paper we analyze the spatio-temporal dynamics of brain tumors. These objects are originally processed from computer tomography images, and can be depicted as a collection of image pixels with varying degrees of color intensity levels. We consider spatio-temporal stochastic processes within a Bayesian framework to model spatial heterogeneity, temporal dependence and spatio-temporal interactions amongst the pixels, providing a general modeling framework for such dynamics. We aim at predicting cancer growth in space and time. We analyze real data on brain tumor based on a set of images taken in several time lags.
eng
Copyright © 2015 Elsevier Ltd. All rights reserved.
Bayesian inference
Gaussian random field
Integrated Nested Laplace Approximations
Space–time modeling
Stochastic partial differential equations
Tumor growth
Bayesian spatio-temporal prediction of cancer dynamics
info:eu-repo/semantics/article