In this paper we investigate the use of image pyramid within a hierarchical registration framework for improved nonlinear image registration. Gaussian image pyramid is commonly used to reduce image complexity so that registration can be performed in a coarse to fine manner. In this
paper, we apply two edge preserving filters, the bilateral filter and the guided filter, to generate image pyramids that can preserve strong gradient so as to improve registration accuracy. In addition, we propose a bilateral fractional differential based image enhancement filter and combine
its output with a guided filter to generate another image pyramid that further enhances the gradient of strong image components. Registration is performed within a hierarchical framework where the model complexity of a Discrete Cosine Transformation (DCT) based nonlinear model is increased
to couple with the image pyramid. Different image pyramids are compared by using three types of synthetic deformation fields. Experimental results show that registration using the gradient enhanced image pyramid achieves more accurate registration than registration using the Gaussian image
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Document Type: Research Article
Publication date: February 14, 2016
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