A unified Bayesian hierarchical model for MRI tissue classification.
Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different ste...
| Publicado en: | Statistics in Medicine Vol. 33; no. 8; pp. 1349 - 1369 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103928396&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103928396 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: Apr2014 vid: 33 iid: 8 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103928396 103928396 NLM24738112 2012549345 NLM24738112 103928396 ppf: 1349 ppct: 20 formats: tig: atl: A unified Bayesian hierarchical model for MRI tissue classification. aug: au: Feng, Dai Liang, Dong Tierney, Luke sug: subj: Probability Brain Anatomy and Histology Brain Mapping Methods Magnetic Resonance Imaging Methods Models, Biological Models, Statistical Human Systems Analysis ab: Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different steps, we extend a previous work to develop a unified Bayesian hierarchical model, which addresses both the partial volume effect and intensity non-uniformity, the two major acquisition artifacts, simultaneously. We adopted a normal mixture model with the means and variances depending on the tissue types of voxels to model the observed intensity values. We modeled the relationship among the components of the index vector of tissue types by a hidden Markov model, which captures the spatial similarity of voxels. Furthermore, we addressed the partial volume effect by construction of a higher resolution image in which each voxel is divided into subvoxels. Finally, We achieved the bias field correction by using a Gaussian Markov random field model with a band precision matrix designed in light of image filtering. Sparse matrix methods and parallel computations based on conditional independence are exploited to improve the speed of the Markov chain Monte Carlo simulation. The unified model provides more accurate tissue classification results for both simulated and real data sets. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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