Abstract

Medical image fusion can combine information from multi-modality images and express them through a single image. How to design a fusion method to preserve more information becomes a hot topic. In this paper, we propose a novel multi-modality medical image fusion method based on Synchronized-Anisotropic Diffusion Equation (S-ADE). First, the modified S-ADE model which is more suitable for Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is employed to decompose two source images. We get the base layers and texture layers. Next, the “Maximum Absolute Value” rule is used for base layers fusion. On texture layers, the fusion decision map is calculated by New Sum of Modified Anisotropic Laplacian (NSMAL) algorithm which is designed using common decomposition coefficients by anisotropic diffusion. Furthermore, the consistency check is constructed on the decision map to mitigate the staircase effect. After that, the fused image is obtained by a simple linear combination of layers. Finally, the fused MR/CT image is obtained after image correction. Its aim is to eliminate redundant texture information which is from MRI images in the contour part. The extensive experimental results demonstrate that the proposed method preserves much information as well as guarantees image quality and visual effects. It outperforms other state-of-the-art methods in terms of subjective and objective evaluations.

Highlights

  • With the development of computer science technology, medical imaging plays a vital role in the clinical diagnosis

  • The objective quantitative indices we select to evaluate the results from two aspects: the amount of salient visual information transferred from source images to their fused image and the visual quality of the fused image

  • In this paper, we propose a medical image fusion method based on SynchronizedAnisotropic Diffusion Equation (S-ADE) model

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Summary

Introduction

With the development of computer science technology, medical imaging plays a vital role in the clinical diagnosis. Magnetic Resonance Imaging (MRI) images primarily depict soft tissues, such as blood vessels [1]. Computed Tomography (CT) images can clearly reflect the precise localization of dense structures [2]. It is nearly impossible to get them both from any single medical modality as they provide information from different aspects with their own advantages. It is necessary to fuse MRI and CT images to meet the requirements of more complex diagnosis, for example, skull base tumor detecting. Medical image fusion has become a widely used tool for creating high-quality images with amounts of information in order to increase the capability

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