A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain
A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain
复制标题
NSCT领域基于相位一致性和局部拉普拉斯能量的多模态医学图像融合方法
DOI：
10.1109/access.2019.2898111
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Xiang, Yan
中科院分区：
文献类型：
--
作者：
Zhu, Zhiqin;Zheng, Mingyao;Xiang, Yan
多模态图像融合在现代医学诊断、遥感、视频监控等领域提供更全面、更精细的信息。提出了一种基于相位一致性和局部拉普拉斯能量的多模态医学图像融合方法。该方法对医学图像对进行非下采样contourlet变换，将源图像分解为高通和低通子带。采用基于相位一致性的融合规则对高通子带进行融合，增强融合图像的细节特征，用于医学诊断。提出了一种基于局部拉普拉斯能量的低通子带融合规则。局部拉普拉斯能量由加权局部能量和加权拉普拉斯系数和组成，分别描述源图像对的结构化信息和详细特征。因此，所提出的融合规则可以同时集成两个关键分量，用于低通子带的融合。将融合后的高通和低通子带进行反变换，得到融合后的图像。在对比实验中，利用三类多模态医学图像对验证了所提方法的有效性。实验结果表明，该方法在图像量和计算成本上都具有较好的性能。
Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, and so on. This paper presents a novel multi-modality medical image fusion method based on phase congruency and local Laplacian energy. In the proposed method, the non-subsampled contourlet transform is performed on medical image pairs to decompose the source images into high-pass and low-pass subbands. The high-pass subbands are integrated by a phase congruency-based fusion rule that can enhance the detailed features of the fused image for medical diagnosis. A local Laplacian energy-based fusion rule is proposed for low-pass subbands. The local Laplacian energy consists of weighted local energy and the weighted sum of Laplacian coefficients that describe the structured information and the detailed features of source image pairs, respectively. Thus, the proposed fusion rule can simultaneously integrate two key components for the fusion of low-pass subbands. The fused high-pass and low-pass subbands are inversely transformed to obtain the fused image. In the comparative experiments, three categories of multi-modality medical image pairs are used to verify the effectiveness of the proposed method. The experiment results show that the proposed method achieves competitive performance in both the image quantity and computational costs.