Multi-modal image registration plays an increasing role in diagnosis, surveillance, and treatment of disease. This paper proposes a new registration measure, called contour and neighbor volume similarity method, which incorporates the merits of both area-based and feature-based methods. The implementation of this integrated method can be illustrated with a coarse-to-fine registration framework. In the coarse registration stage, the closed contours of the objects are first extracted as a stable feature set. Based on a distance measure, the feature set is used to rapidly estimate an initial transformation in the global scope. Subsequently, in response to the possible false alignment when registering symmetrical objects with feature-based methods, we employ an alignment correction procedure to ensure the reliability of the original transformation. Finally, the modified feature neighborhood and mutual information, an area-based method characterized by multiscale filtering mechanism, is adopted in the fine registration stage to obtain a precise final transformation. In addition, we introduce a differential evolution algorithm with an equilibrium strategy for estimating transformation parameters in the coarse registration stage. Our proposed method has been extensively evaluated by comparing with several state-of-the-art registration approaches on multi-modal brain images. The results indicate that it can automatically align images in various environments (different shapes of targets or different noise levels) with high accuracy and robustness.

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Multi-modal image registration plays an increasing role in diagnosis, surveillance, and treatment of disease. This paper proposes a new registration measure, called contour and neighbor volume similarity method, which incorporates the merits of both area-based and feature-based methods. The implementation of this integrated method can be illustrated with a coarse-to-fine registration framework. In the coarse registration stage, the closed contours of the objects are first extracted as a stable feature set. Based on a distance measure, the feature set is used to rapidly estimate an initial transformation in the global scope. Subsequently, in response to the possible false alignment when registering symmetrical objects with feature-based methods, we employ an alignment correction procedure to ensure the reliability of the original transformation. Finally, the modified feature neighborhood and mutual information, an area-based method characterized by multiscale filtering mechanism, is adopted in the fine registration stage to obtain a precise final transformation. In addition, we introduce a differential evolution algorithm with an equilibrium strategy for estimating transformation parameters in the coarse registration stage. Our proposed method has been extensively evaluated by comparing with several state-of-the-art registration approaches on multi-modal brain images. The results indicate that it can automatically align images in various environments (different shapes of targets or different noise levels) with high accuracy and robustness.