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We present a novel three dimensional (3D) region-based hidden Markov model (rbHMM) for unsupervised image
 

Summary: Abstract
We present a novel three dimensional (3D) region-based
hidden Markov model (rbHMM) for unsupervised image
segmentation. Our contributions are twofold. First, our
rbHMM employs a more efficient representation of the
image than approaches based on a rectangular lattice or
grid; thus, resulting in a faster optimization process.
Second, our proposed novel tree-structured parameter
estimation algorithm for the rbHMM provides a locally
optimal data labeling that is invariant to object
rotation. We demonstrate the advantages of our
segmentation technique by validating on synthetic images
of geometric shapes as well as both simulated and clinical
magnetic resonance imaging (MRI) data of the brain. For
the geometric shape data, we show that our method
produces more accurate results in less time than a
grid-based HMM framework using a similar optimization
strategy. For the brain MRI data, our white and gray
matter segmentation results in substantially greater
accuracy than both block-based 3D HMM estimation and

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences