Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain

TLDR

Multimodal medical image fusion combines information from different imaging modalities to aid clinical diagnosis. This paper proposes a novel fusion framework based on non‑subsampled contourlet transform (NSCT). Images are transformed with NSCT, low‑ and high‑frequency components are fused using phase‑congruency and directive‑contrast rules, and the fused image is reconstructed via inverse NSCT. Experiments and comparative studies demonstrate that the framework improves analysis accuracy of multimodal images, as shown in cases of Alzheimer, subacute stroke, and recurrent tumor.

Abstract

<?Pub Dtl=""?> Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on non-subsampled contourlet transform (NSCT). The source medical images are first transformed by NSCT followed by combining low- and high-frequency components. Two different fusion rules based on phase congruency and directive contrast are proposed and used to fuse low- and high-frequency coefficients. Finally, the fused image is constructed by the inverse NSCT with all composite coefficients. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images. Further, the applicability of the proposed framework is carried out by the three clinical examples of persons affected with Alzheimer, subacute stroke and recurrent tumor.