Medical image fusion using enhanced gradient exposure bracketed pairs

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 Gradient Orientation Bracketed pairs. The source medical images are first transformed by Image separation (DOF) followed by combining background and foreground parts frequency components. The Standard Dynamic Range (SDR) device traces image details like contrasts and gradient filter direction in a series of SDR images with different levels. The image overall luminance levels are calculated with the image series, which maximizes the observable distinctions, and then the gradients embedded in these images. The fusion algorithm techniques are used for fusion of images based on Luminance and gradient level. This is done in a multi-resolution of illumination variation in the sequence. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images.