Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model.

Liu Shuaiqi,Zhao Jie,Shi Mingzhu

doi:10.1155/2015/156043

Abstract

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

Highlights

Multimodal medical image fusion is a hot research topic and drives a lot of attention for increasing demands for diagnosis and treatment of diseases
Examples include principal component analysis fusion algorithm (PCA) [4], guided filtering fusion algorithm (GFF) [5], medical image fusion algorithm based on wavelet in [6], fusion algorithm based on Contourlet transform (CT) in [7], fusion algorithms based on nonsubsampled Contourlet transform (NSCT) in [8], fusion algorithm based on Ripplet in [9], and fusion algorithm based on Shearlet and PCNN in [10], and so on
Wang et al proposed a new medical fusion method based on spiking cortical model (SCM) in [11], which can get much better fusion effects; but, in their method, the parameters of SCM are fixed to some constants which will obviously not be widely applicable to all kinds of medical image fusion

Summary

Introduction

Multimodal medical image fusion is a hot research topic and drives a lot of attention for increasing demands for diagnosis and treatment of diseases. Examples include principal component analysis fusion algorithm (PCA) [4], guided filtering fusion algorithm (GFF) [5], medical image fusion algorithm based on wavelet in [6], fusion algorithm based on Contourlet transform (CT) in [7], fusion algorithms based on nonsubsampled Contourlet transform (NSCT) in [8], fusion algorithm based on Ripplet in [9], and fusion algorithm based on Shearlet and PCNN in [10], and so on These methods produce high-quality images, they will lead to loss of information and pixel distortion due to nonlinear operations of fusion rules and blocky artifacts [11].

Rolling Guidance Filter and Spiking Cortical Model

Image Fusion Based on RGF and SCM

Experimental Results

Conclusions