Multimodal Medical Image Fusion Based on Pixel Significance Using Anisotropic Diffusion and Cross Bilateral Filter

Medical image fusion is the process to combine visual information from several medical imaging inputs into a single fused image with no loss of information and distortion. It improves the clinical applications of medical imaging for diagnosis and treatment of medical conditions by retaining complete details in the fused image. In recent years, numerous image fusion techniques have been proposed and shown the significant progress in the field of medical diagnosis. However, fusion performance of these recent techniques is still prone to distortion, blurring and noises. In order to address these problems, this paper proposes a multimodal medical image fusion technique based on anisotropic diffusion and cross bilateral filter (CBF) via pixel significance. First, the method employs edge preserving processing of the original images where it combines linear low pass filter with nonlinear techniques which allow to select meaningful regions of the source images while edges gets preserved. The selection of those regions is based on morphologically processing of linear filters residuals and aims to find the meaningful regions characterized by edges with appropriate size and high amplitude. An anisotropic diffusion is utilized further to decompose images into base and detail layers. The method further proposes to fuse images through weighted average using the estimated weights from the detail images obtained from both base and detail layers using CBF. Lastly, the final fusion result is generated by linear combination of fused images of both layers. Our proposed method is tested with different pairs of publicly available medical image datasets. Experimental results exhibit that the proposed method shows remarkable performance as compares to other existing state-of-the-art methods in terms of both qualitative and quantitative analysis.