School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China.
J Xray Sci Technol. 2010;18(2):157-70. doi: 10.3233/XST-2010-0243.
The fusion of multimodal medical images plays an important role in many clinical applications as it can support more accurate information than any individual source image. This paper presents a novel approach for fusion of computed tomography (CT) and magnetic resonance (MR) images based on wavelet transform. The medical images to be fused are firstly decomposed into multiscale representations by the wavelet transform. Then, by considering the physical meaning of wavelet coefficients and the characteristics of the CT and MR images, the coefficients of the low frequency band and high frequency bands are treated with different schemes: the former is performed with a maximum-selection (MS) rule, and the latter is convolved with a Laplacian operator followed by a MS rule. Finally, the fused image is reconstructed by using the inverse wavelet transform with the combined wavelet coefficients. The performance of our method is qualitatively and quantitatively compared with some existing fusion approaches. The experimental results can demonstrate that the proposed method is a promising and effective technique for fusion of CT and MR images.
多模态医学图像融合在许多临床应用中起着重要作用，因为它可以提供比任何单个源图像更准确的信息。本文提出了一种基于小波变换的 CT 和磁共振（MR）图像融合的新方法。要融合的医学图像首先通过小波变换分解为多尺度表示。然后，通过考虑小波系数的物理意义和 CT 和 MR 图像的特征，对低频带和高频带的系数采用不同的方案进行处理：前者采用最大选择（MS）规则，后者与拉普拉斯算子卷积后再采用 MS 规则。最后，通过使用组合小波系数的逆小波变换重建融合图像。我们的方法的性能与一些现有的融合方法进行了定性和定量比较。实验结果表明，该方法是一种很有前途和有效的 CT 和 MR 图像融合技术。
