Medical Image Quasi-similarity Test

Along with the fast development of the medical imaging technology,more and more anatomic and/or functional medical images could be obtained in clinic.It is always required to register mono-modality and multimodality medical images in order to obtain patients' all-round information,which is helpful in clinical diagnosis and therapy.Medical image registration is a key technology of medical image processing and analysis and has significant meanings in clinical analysis.This dissertation mainly focuses on the registration methods for rigid images, rigid image sequences and non-rigid images.In view of their disadvantages,this dissertation proposes several new and robust registration methods respectively.The main contents of this dissertation are as follows:In order to improve the robustness for rigid images registration,a new similarity measure for medical image registration,called Adaptive Exponential Weighted Mutual Information(AEWMI),is proposed.AEWMI could improve the smoothness and peak keenness performance of similarity measurement curve via adaptively weighting MI according to image quality and resolution.The experimental results,demonstrating the analyzing result,show that our method is robust to image noise or resolution difference and improves the registration robustness effectively.This dissertation innovate the registration method for medical image sequences. A novel registration measure,called as Normalized Higher-order Cross-cumulant Coefficient(NHCC) is proposed.The analytical results show that the proposed NHCC cannot only easily capture the correlation information among the multiple variables simultaneously,but also suppress the additive Gaussian noise influence on the image registration results.While simulation results verify the effectiveness and robustness of the proposed measure,better experimental results of the digital subtraction angiography image registration are also obtained.Under the non-rigid image registration frame,the dissertation focuses on studying an accurate and effective processing method for digital subtraction angiography(DSA) image registration.An image registration method was proposed to be based on the similarity measurement of block image content with self-adaptive selection.In the process of digital subtraction angiography image registration,block image content was classified with blood flow character and for different block image content different similarity measurement was selected with self-adaptation.The analyses and experimental results demonstrated that our method could decrease the influences of the image blurring and vessels on the accuracy of the DSA image registration.The proposed method can effectively improve the robustness and accuracy of DSA image registration.