GIMI: A New Evaluation Index for 3D Multimodal Medical Image Fusion

Abstract

Multimodal medical image fusion plays important roles in clinical applications. Existing indexes used to evaluate 2D medical image fusion algorithms are not suitable for 3D fusions. In this paper, a new evaluation index, which is named as GIMI, is proposed to evaluate and compare the quality of 3D medical image fusion algorithms. GIMI index is based on image volumes not slices. It captures spatial information through the combination of image intensity and gradient, where gradients are computed in 3D space to reconnect all separated image slices together. It treats image slices as a whole of volume to improve the consistency of evaluation. Quantitative and qualitative test results show that GIMI index is effective in evaluating 3D medical image fusions. Its evaluation is consistent with the visual perception of fused images.