基于边缘保留分解和改进稀疏表示的医学图像融合

针对多模态医学图像融合中容易产生伪影且存在细节缺失的问题,提出一种利用多尺度边缘保留分解和稀疏表示的二尺度多模态医学图像融合方法框架.首先利用边缘保留滤波器对源图像进行多尺度分解,得到源图像的平滑层和细节层.然后,将改进的稀疏表示算法用于融合平滑层,并在此基础上提出一种基于图像块筛选的策略来构建过完备字典的数据集,再利...

Medical image fusion based on edge-preserving decomposition and improved sparse representation

Aiming at the problems of artifacts and loss of details in multimodal medical fusion, a two-scale multimodal medical image fusion method framework using multiscale edge-preserving decomposition and sparse representation was proposed. Firstly, the source image was decomposed at multiple scales by utilizing an edge-preserving filter to obtain the smoothing and detail layers of the source image. Then, an improved sparse representation fusion algorithm was employed to fuse the smoothing layers, and on this basis, an image block selection based strategy was proposed to construct the dataset of the over-complete dictionary and the dictionary learning algorithm was used for training the joint dictionary, as well as a novel multi-norm based activity level measurement method was introduced to select the sparse coefficients; the detail layers were merged by an adaptive weighted local regional energy fusion rule. Finally, the fused smoothing layer and detail layers were reconstructed with multi-scale to obtain the fused image. Comparison experiments were conducted on the medical images from three different imaging modalities. The results demonstrate that the proposed method preserves more salient edge features with the improvement of contrast and has advantages in both visual effect and objective evaluation compared to other multi-scale transform and sparse representation methods.