Brain Medical Image Fusion Using L2-Norm-Based Features and Fuzzy-Weighted Measurements in 2-D Littlewood-Paley EWT Domain
Brain Medical Image Fusion Using L2-Norm-Based Features and Fuzzy-Weighted Measurements in 2-D Littlewood-Paley EWT Domain
复制标题
在 2D Littlewood-Paley EWT 域中使用基于 L2 范数的特征和模糊加权测量的脑医学图像融合
DOI：
10.1109/tim.2019.2962849
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发表时间：
2020-08-01
影响因子：
5.6
通讯作者：
Zhou, Wei
中科院分区：
文献类型：
--
作者：
Jin, Xin;Jiang, Qian;Zhou, Wei
计算成像为医学诊断和治疗提供了全面可靠的人体组织信息，医学图像融合是该领域的重要技术之一。经验模式分解（EMD）是一种很有前途的图像处理模型，已被用于图像融合的一些方法。然而，分解层数的变化导致使用EMD进行图像融合的问题。在这篇文章中，我们提出了一种融合医学图像的方法，结合L2范数为基础的功能，匹配/显着性/模糊加权的措施，和2-D Littlewood-Paley经验小波变换（2-D LPEWT）作为新版本的EMD。我们首先用LPEWT对医学图像进行分解，得到残差分量（残差）和详细的子图像，这些子图像被称为本征模式函数（IMF）。然后，我们提取的区域特征的残留与L2范数为基础的模型进行融合的残留，同时融合IMF使用的方法相结合的模糊隶属度函数的匹配/显着性测量。最后对融合后的残差和IMF进行LPEWT逆变换，重构出完整的图像。我们使用一个常用的大脑图像数据集来评估我们的方法。实验结果表明，该方法比传统方法更有效地融合了更多的信息到最终图像中。我们还提出了一个可行的方案，将经验模态分解应用于图像融合。
Computational imaging provides comprehensive and reliable information about human tissue for medical diagnosis and treatment, with medical image fusion as one of the most important technologies in the field. Empirical mode decomposition (EMD), a promising model for image processing, has been used for image fusion in some methods. However, the varying number of decomposed layers leads to problems using EMD for image fusion. In this article, we propose a fusion method for medical images incorporating L2-norm-based features, a match/salience/fuzzy-weighted measure, and the 2-D Littlewood-Paley empirical wavelet transform (2-D LPEWT) as new version of EMD. We first decompose medical images with LPEWT to obtain the residual component (residue) and detailed sub-images that are named as intrinsic mode functions (IMFs). Then we extract the regional features of residue with an L2-norm-based model to fuse the residue while simultaneously fusing IMFs using a method combining a fuzzy membership function with a match/salience measurement. Finally, we reconstruct the comprehensive image by applying inverse LPEWT to the fused residue and IMFs. We evaluated our method using a frequently-used data set of brain images. The results show that our proposed method is more effective than conventional methods by fusing more information into the final images. We also show a feasible scheme for applying EMD to image fusion.