Cortical enhanced tissue segmentation of neonatal brain MR images acquired by a dedicated phased array coil
Name
Shi-2009-Cortical enhanced tissue segmentation of neonatal brain MR images acquired by a dedicated phased array coil.pdf
Size
1.9 MB
Format
Adobe PDF
Checksum (MD5)
d4e131de30a93e3dced8f291ce2b09f5
Author(s)
Shi, Feng
Yap, Pew-Thian
Fan, Yong
Cheng, Jie-Zhi
Wald, Lawrence
Gerig, Guido
Lin, Weili
Shen, Dinggang
Date Issued
August 2009
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Feng Shi et al. “Cortical enhanced tissue segmentation of neonatal brain MR images acquired by a dedicated phased array coil.” Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on. 2009. 39-45. © 2009 IEEE.
Version
Final published version
Abstract
The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2009.5204348
