Medical imaging requires capturing different aspects of a human body. To capture tissues, cartilages, bones and nerves one needs different sensors and different modalities which results in different images from each modality of the same body part. Image fusion is a process by which one can fuse such images from different modalities in a single image. In this paper, a novel DWT-type2 fuzzy method is proposed to fuse two images (CT and MRI images). In this method, initially, the source images are decomposed into low-level and high-level subbands by discrete wavelet transformation (DWT). As the second step, for fusion, Type-2 fuzzy technique is applied on a low-level subband and average fusion method is applied on the high-level subbands in order to enhance the most prominent features present in CT image and MRI image. Finally, the fused low-level subband and highlevel subbands are reconstructed to form the final fused image using inverse-DWT. To test the proposed fusion method, the experiment is carried out on the MRI-T2 image, functional MRI image and CT image. The fused images have been subjectively and objectively evaluated based on certain evaluation measures and the obtained results have been presented in the paper. From the results, one can observe that the proposed method provides an improvement over other primitive fusion methods.

A function h is mentioned as a C -exponential mean labeling of a graph G V , E that has s vertices and r edges if h : V G ⟶ 1 , 2 , 3 , ⋯ , r + 1 is injective and the generated function h ∗ : E G ⟶ 2 , 3 , 4 , ⋯ , r + 1 defined by h ∗ a b = 1 / e h b h b / h a h a 1 / h b − h a , for all a b ∈ E G , is bijective. A graph which recognizes a C -exponential mean labeling is defined as   C -exponential mean graph. In the following study, we have studied the exponential meanness of the path, the graph triangular tree of T n , C m P n , cartesian product of two paths P m ▫ P n , one-sided step graph of S T n , double-sided step graph of 2 S T 2 n , one-sided arrow graph of A r s , double-sided arrow graph of D A r s , and subdivision of ladder graph S L t .