LPPCO: A Novel Multimodal Medical Image Registration Using New Feature Descriptor Based on the Local Phase and Phase Congruency of Different Orientations

At present, many feature descriptors-based registration methods have been proven to be robust in case of complex intensity distortions. However, most of these feature descriptors are only related to intensity information in a patch of neighboring pixels and ignoring the neighbor orientation information, which make the registration performance for medical images with low-resolution appear to be weak robustness and low accuracy. To improve the robustness and accuracy, a novel feature descriptor, named Local-Phase mean and Phase-Congruency values of different Orientations (LPPCO), is developed using filter-bank of Log-Gabor filters at different orientations and frequencies. Next, a similarity measure named LPPCOncc is developed using the normalized cross correlation (NCC) of the LPPCO descriptors, followed by a fast template matching techniques for detecting correspondences between the different images. Additionally, a more sensitivity of phase deviation function is presented for the calculation of phase congruency. The main steps of constructing the similarity measure LPPCOncc include: firstly, we extract the local phase mean and phase congruency values for each pixel in each orientation; secondly, local phase mean orientation histograms and phase congruency values orientation histograms over all the pixels are computed respectively, where the maximum response values are chosen to vote for the corresponding bin; thirdly, combining the two resulting histograms obtains the feature descriptor LPPCO; finally, the LPPCO descriptor is compared across images using NCC. Experimental results show that LPPCOncc is robust to complex intensity distortions between multimodal medical images and outperforms other similarity metrics such as NCC and DLSC. Furthermore, LPPCOncc-based registration algorithm preformed on various types of multimodal medical image pairs shows that it outperforms the NMI-based and DLSC-based registration methods in the registration robustness and accuracy.