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.


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    Title :

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


    Contributors:
    Rongkai Zhao, (author) / Belford, G.G. (author) / Gabriel, M. (author)


    Publication date :

    2004-01-01


    Size :

    332250 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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