A medical image fusion method based on energy classification of BEMD components
A medical image fusion method based on energy classification of BEMD components
复制标题
一种基于BEMD分量能量分类的医学图像融合方法
DOI：
10.1016/j.ijleo.2013.06.075
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发表时间：
2014
期刊：
影响因子：
3.1
通讯作者：
Liu He
中科院分区：
文献类型：
--
作者：
Zhang Baohua;Zhang Zhuanting;Wu Jianshuai;Liu He
提出了一种基于二维经验模式分解（BEMD）和双通道PCNN的医学图像融合方法。将多模态医学图像分解为固有模态函数（IMF）分量和残差分量。IMF分量根据分量能量分为高频分量和低频分量。融合系数采用如下融合规则：高频分量与残差分量叠加得到更多的纹理信息;低频分量包含源图像更多的细节信息，输入双通道PCNN选择融合系数，通过BEMD逆变换得到融合后的医学图像。BEMD是一种分析非线性、非平稳数据的自适应工具，它不需要迭代滤波器或基函数。双通道PCNN降低了计算复杂度，具有良好的融合系数选择能力。BEMD和双通道PCNN的联合应用可以更有效地提取图像的细节信息。实验结果表明，该算法取得了较好的融合效果，与传统的融合算法相比具有更多的优势。
A medical image fusion method based on bi-dimensional empirical mode decomposition (BEMD) and dual-channel PCNN is proposed in this paper. The multi-modality medical images are decomposed into intrinsic mode function (IMF) components and a residue component. IMF components are divided into high-frequency and low-frequency components based on the component energy. Fusion coefficients are achieved by the following fusion rule: high frequency components and the residue component are superimposed to get more textures; low frequency components contain more details of the source image which are input into dual-channel PCNN to select fusion coefficients, the fused medical image is achieved by inverse transformation of BEMD. BEMD is a self-adaptive tool for analyzing nonlinear and non-stationary data; it doesn’t need to predefine filter or basis function. Dual-channel PCNN reduces the computational complexity and has a good ability in selecting fusion coefficients. A combined application of BEMD and dual-channel PCNN can extract the details of the image information more effectively. The experimental result shows the proposed algorithm gets better fusion result and has more advantages comparing with traditional fusion algorithms.
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