P1-84 Hidden markov random field for spatial and spatial-temporal risk mapping

Nonparametric Estimation of Spatial Risk for a Mean Nonstationary Random Field}

The common methods for spatial risk estimation are investigated for a stationary random field. Because of simplifying, lets distribution is known, and parametric variogram for the random field are considered. In this paper, we study a nonparametric spatial method for spatial risk. In this method, we model the random field trend by a local linear estimator, and through bias-corrected residuals, ...

Spatial Risk Mapping for Rare Disease with Hidden Markov Fields and Variational Em by Florence Forbes,

Current risk mapping models for pooled data focus on the estimated risk for each geographical unit. A risk classification, that is, grouping of geographical units with similar risk, is then necessary to easily draw interpretable maps, with clearly delimited zones in which protection measures can be applied. As an illustration, we focus on the Bovine Spongiform Encephalopathy (BSE) disease that ...

A Hidden Spatial-temporal Markov Random Field Model for Network-based Analysis of Time Course Gene Expression Data

Microarray time course (MTC) gene expression data are commonly collected to study the dynamic nature of biological processes. One important problem is to identify genes that show different expression profiles over time and pathways that are perturbed during a given biological process. While methods are available to identify the genes with differential expression levels over time, there is a lac...

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segm...

Planning for Protected Areas Using Spatial and Temporal Metrics