Multi-modality image fusion for medical assistive technology management based on hybrid domain filtering

Abstract

Medical image fusion technique combines images from different imaging modalities to enhance the reliability and accuracy of the medical diagnoses that play an increasingly significant role in clinical applications. In the domain of medical image fusion, it has made huge advancement in the span of past few years. However medical image fusion stills remain a challenging task since the recent methods still suffers from colour distortion, blurring and noise in their fusion results. In order to address the shortcomings of recent methods, this paper presents a novel method to fuse multimodal medical images by employing hybridization of cross bilateral filter and edge aware filtering using pixel-based fusion rule. This method proposed a novel algorithm for computation of weights for final fusion rule. First, a cross bilateral filter (CBF) is employed to both source images which considers both geometric closeness and gray level of neighbourhood pixels by avoiding smoothing the edges and utilizes one image to find the kernel and another to filter and vice versa. Detail images are obtained after subtraction of CBF output from the corresponding source images. With the help of domain transform filter, small scale features of the detail images are retrieved from the surrounding of large-scale structures of these images. These detail images are further processed via rolling guidance filter (RGF) which eliminates small-scale structures and refine edges to preserve large-scale structures of detail images. The weights are computed while measuring the strength of details in the detail images. The computed weights are then directly multiplied with source images followed by weight normalization in order to obtain final fusion result. Qualitative and quantitative experiments are performed on the publicly available datasets, where the results demonstrate that the proposed method outperforms the state-of-the-art, in terms of both visual effect and objective evaluation metrics. Our method generates clear and clean fused images which do not suffers from noise and artifacts while preserving essential information of the multimodal images.