Lei Wang
ABSTRACT
Nonrigid registration plays an important role in reducing potential intensity and geometric differences across individual images with varying modalities. In this paper, we propose a new registration method by introducing two local structural descriptors and a similarity measure. The descriptors are used to highlight small intensity variations and represent the texture characteristics of desirable objects, while the measure estimates image differences based on the introduced descriptors. With these descriptors and measure, multimodal images can be aligned in the free form deformation (FFD) registration framework. Experimental results on two public magnetic resonance (MR) image datasets demonstrated that the developed method can achieve reasonable registration accuracy, and outperformed some existing methods.
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Index Terms
- Nonrigid Registration of Multimodal Images Using Local Structural Descriptors
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