Article

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: 3.1). 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.

0 Followers
 · 
80 Views
  • 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 · 17.47 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new treatment modality – laser induced interstitial thermal therapy (LITT) for treating neurological disorders. Magnetic resonance (MR) guided LITT has recently emerged as a minimally invasive alternative to craniotomy for local treatment of brain diseases (such as glioblastoma multiforme (GBM) and epilepsy). However, LITT is currently only practised as an investigational procedure world-wide due to lack of data on longer term patient outcome following LITT. There is thus a need to quantitatively evaluate treatment related changes between post- and pre-LITT in terms of MR imaging markers. In order to validate LeFiR, we tested the scheme on a synthetic brain dataset (SBD) and in two real clinical scenarios for treating GBM and epilepsy with LITT. Four experiments under different deformation profiles simulating localized ablation effects of LITT on MRI were conducted on 286 pairs of SBD images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid (UniG) placement scheme, a speeded-up robust features (SURF) based method, and a scale-invariant feature transform (SIFT) based method as well as a generic free-form deformation (FFD) approach. The LeFiR method achieved average 90% improvement in recovering the local deformation compared to 82% for the uniform grid placement, 62% for the SURF based approach, and 16% for the generic FFD approach. On the real GBM and epilepsy data, the quantitative results showed that LeFiR outperformed UniG by 28% improvement in average.
    Neurocomputing 11/2014; 144:24–37. DOI:10.1016/j.neucom.2013.11.051 · 2.01 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 · 4.69 Impact Factor

Preview

Download
0 Downloads
Available from