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Proceedings ArticleDOI

Automatic tissue segmentation of neonatal brain MRI

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TLDR
An algorithm for automatic segmentation of neonatal brain MRI is proposed based on expectation maximization of Gaussian mixture model of tissues which shows the maximum probability of being in tissue cluster.
Abstract
In this paper an algorithm for automatic segmentation of neonatal brain MRI is proposed. The first step of the process involves skull stripping and then noise removal using anisotropic diffusion filter. The image is then segmented into three clusters namely white matter, grey matter and cerebrospinal fluid. The segmentation strategy is based on expectation maximization of Gaussian mixture model of tissues. Each pixel is assigned to the tissue cluster which showed the maximum probability of being in. The pixels with same probability likelihoods for more than one cluster are also dealt successfully. The results are evaluated using Dice similarity coefficient. A maximum Dice similarity coefficient of 0.832 is obtained for white matter on T2 images.

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Citations
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Journal ArticleDOI

Sleep across the first year of life is prospectively associated with brain volume in 12-months old infants

TL;DR: In this article , the authors investigated the association between sleep duration and brain volume in infancy and found that infants whose sleep duration decreased less during the first year of life relative to their intercept had, on average, greater white matter volume (β = .36, p = .02).
References
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TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
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Nonlinear anisotropic filtering of MRI data

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TL;DR: It is expected that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains.
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TL;DR: An automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI) that is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.
Journal ArticleDOI

Automatic segmentation and reconstruction of the cortex from neonatal MRI.

TL;DR: An automatic segmentation algorithm detecting mislabeled voxels during cortical segmentation and correcting errors caused by partial volume effects is proposed and results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme.
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