Validation of alternating Kernel mixture method: application to tissue segmentation of cortical and subcortical structures.
This paper describes the application of the alternating Kernel mixture (AKM) segmentation algorithm to high resolution MRI subvolumes acquired from a 1.5T scanner (hippocampus, n = 10 and prefrontal cortex, n = 9) and a 3T scanner (hippocampus, n = 10 and occipital lobe, n = 10). Segmentation of the...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 8p - 9 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2008 Regular issue
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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=105557275&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105557275 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2008 Regular issue pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105557275 105557275 2010050632 NLM18695738 105557275 ppf: 8p ppct: 1 formats: fmt: @attributes: type: P tig: atl: Validation of alternating Kernel mixture method: application to tissue segmentation of cortical and subcortical structures. aug: au: Lee NA Priebe CE Miller MI Ratnanather JT affil: Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA; nayoung@cis.jhu.edu sug: subj: Artificial Intelligence Bioinformatics Cerebral Cortex Anatomy and Histology Diagnostic Imaging Methods Hippocampus Anatomy and Histology Image Interpretation, Computer Assisted Methods Magnetic Resonance Imaging Methods Algorithms Funding Source Image Enhancement Methods Reproducibility of Results Sensitivity and Specificity Validation Studies Human ab: This paper describes the application of the alternating Kernel mixture (AKM) segmentation algorithm to high resolution MRI subvolumes acquired from a 1.5T scanner (hippocampus, n = 10 and prefrontal cortex, n = 9) and a 3T scanner (hippocampus, n = 10 and occipital lobe, n = 10). Segmentation of the subvolumes into cerebrospinal fluid, gray matter, and white matter tissue is validated by comparison with manual segmentation. When compared with other segmentation methods that use traditional Bayesian segmentation, AKM yields smaller errors (P < .005, exact Wilcoxon signed rank test) demonstrating the robustness and wide applicability of AKM across different structures. By generating multiple mixtures for each tissue compartment, AKM mimics the increased variation of manual segmentation in partial volumes due to the highly folded tissues. AKM's superior performance makes it useful for tissue segmentation of subcortical and cortical structures in large-scale neuroimaging studies. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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