Brain tissue segmentation of neonate MR images is a challenging task in study of early brain development, due to low
signal contrast among brain tissues and high intensity variability especially in white matter. Among various brain tissue
segmentation algorithms, the atlas-based segmentation techniques can potentially produce reasonable segmentation
results on neonatal brain images. However, their performance on the population-based atlas is still limited due to the high
variability of brain structures across different individuals. Moreover, it may be impossible to generate a reasonable
probabilistic atlas for neonates without tissue segmentation samples. To overcome these limitations, we present a
neonatal brain tissue segmentation method by taking advantage of the longitudinal data available in our study to establish
a subject-specific probabilistic atlas. In particular, tissue segmentation of the neonatal brain is formulated as two iterative
steps of bias correction and probabilistic atlas based tissue segmentation, along with the guidance of brain tissue
segmentation resulted from the later time images of the same subject which serve as a subject-specific probabilistic atlas.
The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with
manual delineation results. Experimental results show that the utilization of a subject-specific probabilistic atlas can
substantially improve tissue segmentation of neonatal brain images.