The application of multi-modality medical image fusion based method to cerebral infarction

Abstract A multi-modality image fusion can process images of certain organs or issues which were collected from diverse medical imaging equipment. The fusion can extract complementary information and integrate into images with more comprehensive information. The multi-modality image fusion can provide image that was combined with anatomical and physiological information for doctors and bring convenience for diagnosis. Basically, the thesis mainly studies the fusion of MRI and CT images, while taking the cerebral infraction-suffered patients’ images as example. Furthermore, T1 and DWI sequences are respectively carrying on wavelet fusion, pseudo color fusion, and α channel fusion. Meanwhile, the numerous image data will be objectively assessed and compared from several aspects such as information entropy, mutual information, the mean grads, and spatial frequency. By means of the observation and analysis, compared with original image, it can be figured out that fused image not only has richer details but also more clearly highlights the lesions of cerebral infarction