Gaussian mixture model-based segmentation of MR images taken from premature infant brains.
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.
Merisaari
Harri
H
Department of Information Technology and Turku Centre for Computer Science (TUCS), FI-20014 University of Turku, Finland. harri.merisaari@utu.fi
Parkkola
Riitta
R
Alhoniemi
Esa
E
Teräs
Mika
M
Lehtonen
Liisa
L
Haataja
Leena
L
Lapinleimu
Helena
H
Nevalainen
Olli S
OS
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D007234:Infant, Premature; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity