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Proceedings Paper

A novel framework for multi-modal intensity-based similarity measures based on internal similarity
Author(s): Graeme P. Penney; Lewis D. Griffin; Andrew P. King; David J. Hawkes
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Paper Abstract

We present a novel framework for describing intensity-based multi-modal similarity measures. Our framework is based around a concept of internal, or self, similarity. Firstly the locations of multiple regions or patches which are "similar" to each other are identified within a single image. The term "similar" is used here to represent a generic intra-modal similarity measure. Then if we examine a second image in the same locations, and this image is registered to the first image, we should find that the patches in these locations are also "similar", though the actual features in the patches when compared between the images could be very different. We propose that a measure based on this principle could be used as an inter-modal similarity measure because, as the two images become increasingly misregistered then the patches within the second image should become increasingly dissimilar. Therefore, our framework results in an inter-modal similarity measure by using two intra-modal similarity measures applied separately within each image. In this paper we describe how popular multi-modal similarity measures such as mutual information can be described within this framework. In addition the framework has the potential to allow the formation of novel similarity measures which can register using regional information, rather than individual pixel/voxel intensities. An example similarity measure is produced and its ability to guide a registration algorithm is investigated. Registration experiments are carried out using three datasets. The pairs of images to be registered were specifically chosen as they were expected to challenge (i.e. result in misregistrations) standard intensity-based measures, such as mutual information. The images include synthetic data, cadaver data and clinical data and cover a range of modalities. Our experiments show that our proposed measure is able to achieve accurate registrations where standard intensity-based measures, such as mutual information, fail.

Paper Details

Date Published: 11 March 2008
PDF: 10 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140X (11 March 2008); doi: 10.1117/12.769402
Show Author Affiliations
Graeme P. Penney, King's College London (United Kingdom)
Lewis D. Griffin, Univ. College London (United Kingdom)
Andrew P. King, King's College London (United Kingdom)
David J. Hawkes, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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