A robust point-matching algorithm for autoradiograph alignment
1997; Elsevier BV; Volume: 1; Issue: 4 Linguagem: Inglês
10.1016/s1361-8415(97)85008-6
ISSN1361-8431
AutoresAnand Rangarajan, Haili Chui, Eric Mjolsness, Suguna Pappu, Lila Davachi, Patricia S. Goldman‐Rakic, James S. Duncan,
Tópico(s)Morphological variations and asymmetry
ResumoWe present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local, natural and artifactual differences between the autoradiograph slices. We have used the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated its robustness property using synthetically generated spatial mappings and provided an anecdotal visual comparison with the well-known iterated closest-point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.
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