A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Gaussian mixture model-based segmentation of MR images taken from premature infant brains
Tekijät: Merisaari H, Parkkola R, Alhoniemi E, Teras M, Lehtonen L, Haataja L, Lapinleimu H, Nevalainen OS
Kustantaja: ELSEVIER SCIENCE BV
Julkaisuvuosi: 2009
Journal: Journal of Neuroscience Methods
Tietokannassa oleva lehden nimi: JOURNAL OF NEUROSCIENCE METHODS
Lehden akronyymi: J NEUROSCI METH
Vuosikerta: 182
Numero: 1
Aloitussivu: 110
Lopetussivu: 122
Sivujen määrä: 13
ISSN: 0165-0270
DOI: https://doi.org/10.1016/j.jneumeth.2009.05.026
Tiivistelmä
Segmentation of Magnetic Resonance multi-layer images of premature infant brain has additional challenges in comparison to normal adult brain segmentation. Images of premature infants contain lower signal to noise ratio due to shorter scanning times. Further, anatomic structure include still greater variations which can impair the accuracy of standard brain models. A fully automatic brain segmentation method for T1-weighted images is proposed in present paper. The method uses watershed segmentation with Gaussian mixture model clustering for segmenting cerebrospinal fluid from brain matter and other head tissues. The effect of the myelination process is considered by utilizing information from T2-weighted images. The performance of the new method is compared voxel-by-voxel to the corresponding expert segmentation. The proposed method is found to produce more uniform results in comparison to three accustomary segmentation methods originally developed for adults. This is the case in particular when anatomic forms are still under development and differ in their form from those of adults. (C) 2009 Elsevier B.V. All rights reserved.
Segmentation of Magnetic Resonance multi-layer images of premature infant brain has additional challenges in comparison to normal adult brain segmentation. Images of premature infants contain lower signal to noise ratio due to shorter scanning times. Further, anatomic structure include still greater variations which can impair the accuracy of standard brain models. A fully automatic brain segmentation method for T1-weighted images is proposed in present paper. The method uses watershed segmentation with Gaussian mixture model clustering for segmenting cerebrospinal fluid from brain matter and other head tissues. The effect of the myelination process is considered by utilizing information from T2-weighted images. The performance of the new method is compared voxel-by-voxel to the corresponding expert segmentation. The proposed method is found to produce more uniform results in comparison to three accustomary segmentation methods originally developed for adults. This is the case in particular when anatomic forms are still under development and differ in their form from those of adults. (C) 2009 Elsevier B.V. All rights reserved.
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