Li Xilai, Li Xiaosong
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China.
Front Neurosci. 2023 Jul 13;17:1204263. doi: 10.3389/fnins.2023.1204263. eCollection 2023.
As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM.
作为一种重要的面向临床的信息融合技术，多模态医学图像融合将来自不同模态图像的有用信息整合到一幅综合的融合图像中。然而，现有方法在融合低频或基础层时通常只考虑能量信息，而忽略了在能量值较低的像素中可能存在有用纹理信息这一事实。因此，可能会将错误的纹理引入融合结果中。为了解决这个问题，我们提出了一种基于错误纹理去除的新型多模态脑图像融合算法。首先实施一种两层分解方案来生成高频和低频子带。我们提出了一种基于梯度差和熵的显著特征检测算子。所提出的算子将高频子带中的梯度差和信息量整合起来，以有效地识别清晰的细节信息。随后，我们通过将每个像素的局部相位特征用作强度测量并使用随机游走算法来检测能量信息，从而检测低频子带的能量信息。最后，我们提出了一种滚动引导滤波迭代最小二乘模型来重建低频分量中的纹理信息。通过大量实验，我们成功证明了所提出的算法优于一些现有最先进的方法。我们的源代码可在https://github.com/ixilai/ETEM上公开获取。
