Medical image fusion using content based automatic segmentation

Image fusion is a process of combining complementary information from multi modality images of the same patient in to an image. Hence the resultant image consists of more informative than the individual images alone. In this paper, a novel feature level image fusion is proposed. In feature level fusion, source images are segmented into regions and features like pixel intensities, edges or texture are used for fusion. The feature level image fusion with region based would be more meaningful than the pixel based fusion methods. The proposed fusion method contains three steps. Firstly, the multi modal images are segmented into regions using automatic segmentation process. Secondly the images are fused according to region based fusion rule. Finally the regions are merged together to acquire final fused image. The performance of the proposed method can be evaluated with fusion symmetry, peak signal to noise ratio both quantitatively and qualitatively.