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Proceedings Paper

How to produce a landmark point: the statistical geometry of incompletely registered images
Author(s): Fred L. Bookstein
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Paper Abstract

The thin-plate spline can unwarp a data set of landmark-labelled medical images so that their landmarks are exactly superposed over an average landmark configuration. This pixel relabeling is highly nonlinear in the original image data. Nevertheless, for most biostatistical purposes, the resulting unwarped images can be treated as if they arose from raw measurements (in this case, pixel-by-pixel gray levels) by a covariate adjustment suppressing unwanted variation. Tasks of discrimination and classification of images can benefit greatly from the augmented precision of subsequent quantitative comparisons. These `adjusted mean differences'--pixelwise group mean differences of the unwarped images--may be combined with differences of landmark shape in prescriptions for new landmark locations that further sharpen the unwarping or classification. These considerations are exemplified in a detailed analysis of some midsagittal brain images of medical students and schizophrenics.

Paper Details

Date Published: 11 August 1995
PDF: 12 pages
Proc. SPIE 2573, Vision Geometry IV, (11 August 1995); doi: 10.1117/12.216437
Show Author Affiliations
Fred L. Bookstein, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 2573:
Vision Geometry IV
Robert A. Melter; Angela Y. Wu; Fred L. Bookstein; William D. K. Green, Editor(s)

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