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A cluster-assisted global optimization method for high resolution medical image registration

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3 Author(s)
Rongkai Zhao ; Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA ; Belford, G.G. ; Gabriel, M.

Optimization is a key component of image registration. Due to the non-convexity and high computation cost of the objective function, a common tactic is to set an initial guess and then use multi-resolution or local optimization methods to find a local optimum of the objective function. For almost all local optimization methods, the initial location in the search space plays a critical role in the accuracy of the registration. Initial guesses are often obtained through data-specific methods. The paper offers a new hybrid optimization method assisted by a density-based clustering algorithm. The new method is less data-specific and more suitable for semi-automatic or automatic image registration. Global optimization does not guarantee timely convergence. A genetic algorithm is a component of our hybrid method; however, our method usually converges within a reasonable time. This new method has been applied to registering high resolution brain images.

Published in:

Image Analysis and Interpretation, 2004. 6th IEEE Southwest Symposium on

Date of Conference:

28-30 March 2004