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Automatic Localization of Anatomical Point Landmarks for Brain Image Processing Algorithms

Automatic Localization of Anatomical Point Landmarks for Brain Image Processing Algorithms 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Automatic Localization of Anatomical Point Landmarks for Brain Image Processing Algorithms

Neu, Scott; Toga, Arthur
Neuroinformatics , Volume 6 (2) – May 30, 2008
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14 pages

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Publisher
Springer Journals
Copyright
Copyright © 2008 by Humana Press
Subject
Biomedicine; Computational Biology/Bioinformatics; Biotechnology; Neurology ; Computer Appl. in Life Sciences ; Neurosciences
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-008-9018-x
pmid
18512163
Publisher site
See Article on Publisher Site

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.

Journal

Neuroinformatics – Springer Journals

Published: May 30, 2008

References

  • Least square fitting of two 3D point sets
    Arun, K. S.; Huang, T. S.
  • Interactive live-wire boundary extraction
    Barrett, W. A.; Mortensen, E. N.
  • A method for registration of 3-D shapes
    Besl, P. J.; McKay, N. D.
  • Interactive graph cuts for optimal boundary and region segmentation of objects in N–D images
    Boykov, Y.; Jolly, M.
  • A technique for the deidentification of structural brain MR images
    Bischoff-Grethe, A.; Fischl, B.
  • Segmentation by textures using correlation
    Chen, P. C.; Pavlidis, T.
  • Ratio regions: A technique for image segmentation
    Cox, I. J.; Rao, S. B.
  • Interactive Outlining: An improved approach using active contours
    Daneels, D.; Campenhout, D.
  • Texture analysis using generalized cooccurrence matrices
    Davis, L. S.; Johns, S. A.
  • Optimization and evaluation of landmark-based image correlation
    Ende, G.; Treuer, H.
  • User-steered image segmentation paradigms: Live wire and live lane
    Falco, A. X.; Udupa, J. K.
  • Volume morphing methods for landmark based 3D image deformation
    Fang, S.; Raghavan, R.
  • Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain
    Fischl, B.; Salat, D. H.
  • Automatic landmarking of magnetic resonance brain images
    Fitzpatrick, J. M.; Reinhardt, J. M.
  • Localization of 3d anatomical point landmarks in 3d tomographic images using deformable models
    Frantz, S.; Rohr, K.
  • SVD-based approach to transparent embedding data into digital images
    Gorodetski, V. I.; Popyack, L. J.
  • Automatic registration of brain magnetic resonance images based on talairach reference system
    Han, Y.; Park, H.
  • FIR filter representation of reduced-rank noise reduction
    Hansen, P. C.; Jensen, S. H.
  • Evaluation of 3D operators for the detection of anatomical point landmarks in MR and CT Images
    Hartkens, T.; Rohr, K.
  • Registration of MR and CT images for skull base surgery using point-like anatomical features
    Hill, D. L.; Hawkes, D. J.
  • Image restoration by singular value decomposition
    Huang, T.; Narendra, P.
  • A singularly valuable decomposition: The SVD of a matrix
    Kalman, D.
  • Snakes: Active contour models
    Kass, M.; Witkin, A.
  • Application of SVD-based spatial filtering to video sequences
    Konstantinides, K.; Yovanof, G. S.
  • Noise estimation and filtering using block-based singular value decomposition
    Konstantinides, K.; Natarajan, B.
  • Using local extremum curvatures to extract anatomical landmarks from medical images
    Briquer, L.; Lachmann, F.
  • Multiple surface identification and matching in magnetic resonance imaging
    MacDonald, D.; Avis, D.; Evan, A. C.
  • User-defined b-spline template-snakes
    McInerney, T.; Dehmeshki, H.
  • Morphological segmentation
    Meyer, F.; Beucher, S.
  • Digital image compression by outer product expansion
    O’Leary, D. P.; Peleg, S.
  • Handbook of medical imaging
    Pennec, X.; Ayache, N.
  • Three-dimensional multi-modal image-guidance for neurosurgery
    Peters, T. M.; Davey, B. L. K.
  • Numerical recipies in C: The art of scientific computing
    Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P.
  • On 3d differential operators for detecting point landmarks
    Rohr, K.
  • Landmark-based image analysis using geometric and intensity models
    Rohr, K.
  • The image processing handbook
    Russ, J. C.
  • Level set methods and fast marching methods
    Sethian, J. A.
  • Low dimensional procedure for the characterization of human faces
    Sirovich, L.; Kirby, M.
  • Anatomic landmark image registration: Validation and comparison
    Strasters, K. C.; Little, J. A.
  • Extremal points: Definition and application to 3D image registration
    Thirion, J.
  • Brain warping
    Toga, A. W.
  • Eigenfaces for recognition
    Turk, M. A.; Pentland, A. P.
  • Automated image registration: I. General methods and intrasubject, intramodality validation
    Woods, R. P.; Grafton, S. T.
  • 3D parametric intensity models for the localization of different types of 3D anatomical point landmarks in tomographic images
    Wörz, S.; Rohr, K.
  • User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
    Yushkevich, P. A.; Piven, J.