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Applications of Optimizing Neural Networks in Medical Image Registration

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Artificial Neural Networks in Medicine and Biology

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

We formulate feature-based medical image registration as a point matching problem. Point matching requires us to solve for the (rigid or non-rigid) spatial mapping that brings one point-set into register with the other. In order to solve for the spatial mapping, we require the point-to-point correspondences between the two point-sets. Since this information is typically unavailable, we formulate the problem w.r.t. the spatial mapping and the correspondences as a mixed variable objective function. Deterministic annealing and the softassign are used to obtain a robust point matching algorithm. Results are shown for 2D rigid autoradiograph alignment and 3D non-rigid mapping of cortical anatomical MR brain structures.

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Author information

Authors and Affiliations

  1. Depts. of Diagnostic Radiology and Electrical Engineering, Yale University, New Haven, CT, USA

    Anand Rangarajan & Haili Chui

Authors
  1. Anand Rangarajan
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  2. Haili Chui
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Editor information

Editors and Affiliations

  1. Department of Philosophy, Göteborg University, Box 200, SE-405 30, Göteborg, Sweden

    Helge Malmgren BA, PhD, MD

  2. Department of Electrical Engineering, Linköping University, SE-585 83, Linköping, Sweden

    Magnus Borga MSc, PhD

  3. Department of Computer Science, University of Skövde, PO Box 408, SE-541 28, Skövde, Sweden

    Lars Niklasson BSc, MSc, PhD

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© 2000 Springer-Verlag London

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Rangarajan, A., Chui, H. (2000). Applications of Optimizing Neural Networks in Medical Image Registration. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_13

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  • DOI: https://doi.org/10.1007/978-1-4471-0513-8_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-289-1

  • Online ISBN: 978-1-4471-0513-8

  • eBook Packages: Springer Book Archive

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