Abstract
Alignment of multimodality images is the process that attempts to find the geometric transformation overlapping at best the common part of two images. The process requires the definition of a similarity measure and a search strategy. In the literature, several studies have shown the ability and effectiveness of entropy-based similarity measures to compare multimodality images. However, the employed search strategies are based on some optimization schemes which require a good initial guess. A combinatorial optimization method is critically needed to develop an effective search strategy. Artificial Immune Systems (AIS S ) have been proposed as a powerful addition to the canon of meta-heuristics. In this paper, we describe a framework which combines the use of an entropy-based measure with an AIS-based search strategy. We show how AIS S have been tailored to explore efficiently the space of transformations. Experimental results are very encouraging and show the feasibility and effectiveness of the proposed approach.
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Bendiab, E., Meshoul, S., Batouche, M. (2003). An Artificial Immune System for Multimodality Image Alignment. In: Timmis, J., Bentley, P.J., Hart, E. (eds) Artificial Immune Systems. ICARIS 2003. Lecture Notes in Computer Science, vol 2787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45192-1_2
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DOI: https://doi.org/10.1007/978-3-540-45192-1_2
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