Abstract
The medical image registration algorithm uses the mutual information measure function that has many local extremes. Therefore, we propose our medical image registration algorithm that combines generalized mutual information with PSO-Powell hybrid algorithm and uses the objective measure function based on Renyi entropy. The Renyi entropy can remove the local extremes. We use the particle swarm optimization (PSO) algorithm to locate the measure function near the local extremes. Then we take the local extremes as initial point and use the Powell optimization algorithm to search for the global optimal solution. Section 2.2 of the paper presents the six-step procedure of our registration algorithm. We simulate medical image data with the registration algorithm; the simulation results, given in Table. 2 and 3, show preliminarily that the registration algorithm can eliminate the local extremes of objective measure function and accelerate the convergence rate, thus obtaining accurate and better registration results.
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Zhang, J., Huo, P., Teng, J., Wang, X., Wang, S. (2010). Medical Image Registration Algorithm with Generalized Mutual Information and PSO-Powell Hybrid Algorithm. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_20
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DOI: https://doi.org/10.1007/978-3-642-13495-1_20
Publisher Name: Springer, Berlin, Heidelberg
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