11 August 1995 How to produce a landmark point: the statistical geometry of incompletely registered images
Author Affiliations +
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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fred L. Bookstein, "How to produce a landmark point: the statistical geometry of incompletely registered images", Proc. SPIE 2573, Vision Geometry IV, (11 August 1995); doi: 10.1117/12.216437; https://doi.org/10.1117/12.216437
PROCEEDINGS
12 PAGES


SHARE
RELATED CONTENT

A framework for joint image-and-shape analysis
Proceedings of SPIE (March 21 2014)
White matter fiber-based analysis of T1w/T2w ratio map
Proceedings of SPIE (February 24 2017)
Spline-based deformable models
Proceedings of SPIE (August 11 1995)

Back to Top