School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
Med Biol Eng Comput. 2018 Sep;56(9):1565-1578. doi: 10.1007/s11517-018-1796-1. Epub 2018 Feb 13.
In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images. Graphical abstract The detailed implementation of the proposed medical image fusion algorithm.
本文提出了一种基于多尺度联合分解框架（MJDF）和剪切滤波器（SF）的细节增强型多模态医学图像融合算法。该算法使用梯度最小化平滑滤波器（GMSF）和高斯低通滤波器（GLF）构建的 MJDF，可将源图像分解为多个尺度的低通层、边缘层和细节层。为了突出融合图像中的细节信息，对每个尺度的边缘层和细节层进行加权组合，得到细节增强层。由于方向滤波器在捕获显著信息方面非常有效，因此在细节增强层中应用 SF 以提取几何特征并获得方向系数。设计了基于视觉显著度图的融合规则来融合低通层，并且使用标准差之和作为方向系数融合的活动水平度量。通过合成融合的低通层和方向系数，得到最终的融合结果。实验结果表明，该方法具有平移不变性、方向选择性和细节增强特性，能够有效地保留和增强多模态医学图像的细节信息。
