Automatic Localization of Anatomical Point Landmarks for Brain Image Processing Algorithms

Department 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.
Neuroinformatics (Impact Factor: 2.83). 02/2008; 6(2):135-48. DOI: 10.1007/s12021-008-9018-x
Source: PubMed

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.

17 Reads
  • Source
    • "Beaulieu (2001) has elaborated on many of these characteristics and how they relate to what constitutes a trusted neuroscience digital resource. Additional issues involve HIPAA compliance (Kulynych 2002), concern over incidental findings (Illes, Kirschen et al. 2006), anonymization of facial features (Bischoff-Grethe, Fischl et al. 2004; Neu and Toga 2008), and skull stripping (Zhuang, Valentino et al. 2006). Given the degree of effort required to curate active data deposition as well as for comprehensively addressing these issues, the most trusted archives tend to be those whose infrastructure and archival processes are sufficiently mature and specifically dedicated to the goals of long term community-oriented databasing. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.
    NeuroImage 05/2009; 47(4):1720-34. DOI:10.1016/j.neuroimage.2009.03.086 · 6.36 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
    Journal of Cognitive Neuroscience 01/1991; 3(1):71-86. DOI:10.1162/jocn.1991.3.1.71 · 4.09 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The Alzheimer's Diseases Neuroimaging Initiative project has brought together geographically distributed investigators, each collecting data on the progression of Alzheimer's disease. The quantity and diversity of the imaging, clinical, cognitive, biochemical, and genetic data acquired and generated throughout the study necessitated sophisticated informatics systems to organize, manage, and disseminate data and results. We describe, here, a successful and comprehensive system that provides powerful mechanisms for processing, integrating, and disseminating these data not only to support the research needs of the investigators who make up the Alzheimer's Diseases Neuroimaging Initiative cores, but also to provide widespread data access to the greater scientific community for the study of Alzheimer's Disease.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 05/2010; 6(3):247-56. DOI:10.1016/j.jalz.2010.03.001 · 12.41 Impact Factor
Show more


17 Reads
Available from