A more precise multi-modal image registration using self-similarities
Qiu Guan, Qinqin Jin, Haixia Long, Kangjie Li, Haigen Hu, Xiaoyan Wang · 2017
With the development of medical imaging technology, an increasing number of images of different modalities are used in diagnosis and fused together for getting more information. Multi-modality image registration is the key technology of multi-modal image fusion. It is also a basic topic in computer vision and widely used in image analysis. However, the images of different modalities would not show the same representation. Therefore, images of different modalities cannot be aligned directly with each other. Currently, a new descriptor which is calculated from different modality images by self-similarities context has been proposed. This descriptor makes images of different modalities align with each other directly. However, it is still affect by noise. In this paper, a new framework of multimodal registration is proposed to improve the method of multimodal image registration with self-similarities contexts. Firstly, a series of operations of de-noise is implemented in images before registration. Then a new strategy of filter is proposed in the later image registration. Experiment shows that, this framework of image registration is more robust and precise than the original one.