Segmentation-Based Fusion of CT and MR Images

In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from the CT image. This skull information is used to extract soft tissue information from the MR image. Both the skull information and the soft tissue information are then fused using the fusion rule. The efficiency of the proposed method over other state-of-the-art fusion methods is analyzed and compared using qualitative and quantitative analysis methods. Qualitative analysis shows the improvement in the contrast between the bone and the soft tissue using the proposed method over other state-of-the-art methods without introducing any artifacts or distortions. Classical and gradient-based quantitative analysis also show significant improvement in the fused image obtained using the proposed method over the five state-of-the-art methods. The percentage improvement in the standard deviation, average gradient, entropy, spatial frequency, QABF, and LABF of the proposed method over the best value obtained by the five state-of-the-art methods is 27.11%, 12.06%, 23.64%, 11.30%, 5.59%, and 13.70% respectively.