Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

DOI10.1016/j.patcog.2004.02.012zbMath1070.68597OpenAlexW2150782396MaRDI QIDQ1764074

Geoffrey J. McLachlan, Shu-Kay Ng

Publication date: 23 February 2005

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2004.02.012

zbMATH Keywords

EM algorithmStatistical pattern recognitionMixture modelsImage segmentationMagnetic resonance imagingHidden Markov random fieldMultiresolution \(k\)d-treesSparse incremental EM algorithm

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (1)

Mini-batch learning of exponential family finite mixture models

Cites Work

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• Optimal time bounds for approximate clustering
• EM procedures using mean field-like approximations for Markov model-based image segmentation
• Accelerating EM for large databases
• On random sampling without replacement from a finite population

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