Multiscale Medical Image Fusion in Wavelet Domain

Rajiv Singh,Ashish Khare

doi:10.1155/2013/521034

Abstract

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach.

Highlights

The development of multimodality medical imaging sensors for extracting clinical information has influenced to explore the possibility of data reduction and having better visual representation
The fusion results have been shown for three sets of medical image pairs of size 256 × 256 shown in Figures 3(a), 3(b), 4(a), 4(b), 5(a), and 5(b), respectively
On observing the third set of medical images (CT and magnetic resonance imaging (MRI)) and fusion results for these images which are shown in Figures 5(a)–5(o), one can verify the fact that again the proposed method has been found superior in terms of visual representation over gradient pyramid (GP), contrast pyramid (CP), ratio pyramid (RP), principal component analysis (PCA), discrete wavelet transform (DWT) with DBSS, and SIDWT with Haar fusion methods

Summary

Introduction

The development of multimodality medical imaging sensors for extracting clinical information has influenced to explore the possibility of data reduction and having better visual representation. One of the major disadvantages of spatial domain fusion method is that it introduces spatial distortions in the resultant fused image and does not provide any spectral information. Since medical images are generally of poor contrast, the spatial information should be preserved in the medical images without introducing any distortion or noise These requirements of medical images are better preserved in transform domain fusion. A simple DWT-based medical image fusion, which follows weighted fusion rule, has been introduced by Cheng et al [21]. Another pixel- and region-based multiresolution image fusion for MRI and CT image is discussed in [22].

Wavelet Transform and Image Fusion

The Proposed Fusion Approach

Fusion Results and Discussions

Conclusions