MRI and CT Image Fusion using Synchronized Anisotropic Diffusion Equation with DT-CWT Decomposition

Vijayalakshmi Aakaaram, Srinvas Bachu
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Abstract

Medical image fusion plays the major role in many applications including brain tumor segmentation, and classification. But the conventional methods are suffering with colour artifacts. Thus, this article proposes a novel magnetic resonance imaging (MRI) and computerized tomography (CT) based multi modal medical image fusion using synchronized anisotropic diffusion equation (SADE) with dual tree dual-tree complex wavelet transform (DT-CWT) decomposition. Initially, source images are divided into multiple bands by using DT-CWT approach. In addition, SADE process is applied to extract the approximate and detailed layers. Further, principal component analysis (PCA) is applied to extract the eigen vectors. Finally, PCA fusion rule is applied to get the fused outcome. The simulation results show that proposed fusion results shows better subjective and object performance as compared to conventional fusion methods.
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基于同步各向异性扩散方程和DT-CWT分解的MRI和CT图像融合
医学图像融合在脑肿瘤分割、分类等诸多应用中发挥着重要作用。但是传统的方法受到彩色伪影的影响。为此,本文提出了一种基于同步各向异性扩散方程(SADE)和双树双树复小波变换(DT-CWT)分解的基于磁共振成像(MRI)和计算机断层扫描(CT)的多模态医学图像融合方法。首先,利用DT-CWT方法将源图像划分为多个波段。此外,应用SADE过程提取近似层和详细层。在此基础上,应用主成分分析(PCA)提取特征向量。最后,应用PCA融合规则得到融合结果。仿真结果表明,与传统的融合方法相比,所提出的融合方法具有更好的主客体性能。
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