Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Neuroinformatics. 2008 Summer;6(2):135-48. doi: 10.1007/s12021-008-9018-x. Epub 2008 May 30.

Automatic localization of anatomical point landmarks for brain image processing algorithms.

Author information

  • 1Department of Neurology, UCLA Laboratory of Neuro Imaging, David Geffen School of Medicine, Suite 225, 635 Charles Young Drive South, Los Angeles, CA 90095-7334, USA.

Abstract

Many brain image processing algorithms require one or more well-chosen seed points because they need to be initialized close to an optimal solution. Anatomical point landmarks are useful for constructing initial conditions for these algorithms because they tend to be highly-visible and predictably-located points in brain image scans. We introduce an empirical training procedure that locates user-selected anatomical point landmarks within well-defined precisions using image data with different resolutions and MRI weightings. Our approach makes no assumptions on the structural or intensity characteristics of the images and produces results that have no tunable run-time parameters. We demonstrate the procedure using a Java GUI application (LONI ICE) to determine the MRI weighting of brain scans and to locate features in T1-weighted and T2-weighted scans.

PMID:
18512163
[PubMed - indexed for MEDLINE]
PMCID:
PMC3113710
Free PMC Article

Images from this publication.See all images (13)Free text

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Springer Icon for PubMed Central
    Loading ...
    Write to the Help Desk