Piecewise affine registration of biological images

Alain Pitiot, Grégoire Malandain, Eric Bardinet, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

18 Scopus citations

Abstract

This manuscript tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or different modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that can be modeled as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using real biomedical images, including MRI, histology and cryosection data.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJames C. Gee, J. B. Antoine Maintz, Michael W. Vannier
PublisherSpringer Verlag
Pages91-101
Number of pages11
ISBN (Print)3540203435, 9783540203438
DOIs
StatePublished - 2003
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2717
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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