Inception Based Medical Image Registration

Biological tissue has strong absorbability and strong scattering, which may lead to edge blur, low signal-to-noise ratio and so on, which affects the registration of medical images. This chapter simulates and makes the biological tissue imitation image data set under the above conditions, cleverly designs a regression structure embedded in the U-net Inception module, and forms an unsupervised deep learning medical image registration method. It is applied to the biological tissue imitation data set for the first time. The experimental results show that the image registration method based on SIFT, ORB, BRISK, AKAZE and so on can not successfully complete the registration work because it can not find a sufficient number of effective key points on the data set, SURF although it can complete the registration work. But the effect is weaker than the proposed method. Based on the medical image registration method proposed by Inception, this chapter can effectively solve the problem of biological tissue image registration with strong absorption and strong scattering.