Share Email Print
cover

Proceedings Paper

Brain tissue segmentation of neonatal MR images using a longitudinal subject-specific probabilistic atlas
Author(s): Feng Shi; Yong Fan; Songyuan Tang; John Gilmore; Weili Lin; Dinggang Shen
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725942 (27 March 2009); doi: 10.1117/12.811610
Show Author Affiliations
Feng Shi, Univ. of North Carolina, Chapel Hill (United States)
Yong Fan, Univ. of North Carolina, Chapel Hill (United States)
Songyuan Tang, Univ. of North Carolina, Chapel Hill (United States)
John Gilmore, Univ. of North Carolina, Chapel Hill (United States)
Weili Lin, Univ. of North Carolina, Chapel Hill (United States)
Dinggang Shen, Univ. of North Carolina, Chapel Hill (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

© SPIE. Terms of Use
Back to Top