School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Comput Math Methods Med. 2020 Jan 24;2020:3290136. doi: 10.1155/2020/3290136. eCollection 2020.
Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion results due to the loss of detailed feature information during fusion. To deal with it, this paper proposes a new multimodal medical image fusion method based on the imaging characteristics of medical images. In the proposed method, the non-subsampled shearlet transform (NSST) decomposition is first performed on the source images to obtain high-frequency and low-frequency coefficients. The high-frequency coefficients are fused by a parameter-adaptive pulse-coupled neural network (PAPCNN) model. The method is based on parameter adaptive and optimized connection strength adopted to promote the performance. The low-frequency coefficients are merged by the convolutional sparse representation (CSR) model. The experimental results show that the proposed method solves the problems of difficult parameter setting and poor detail preservation of sparse representation during image fusion in traditional PCNN algorithms, and it has significant advantages in visual effect and objective indices compared with the existing mainstream fusion algorithms.
医学图像的视觉效果对临床辅助诊断有很大的影响。目前，医学图像融合已经成为一种强有力的临床应用手段。传统的医学图像融合方法由于在融合过程中丢失了详细的特征信息，导致融合效果较差。针对这一问题，本文提出了一种基于医学图像成像特征的新的多模态医学图像融合方法。在提出的方法中，首先对源图像进行非下采样剪切波变换（NSST）分解，得到高频和低频系数。通过参数自适应脉冲耦合神经网络（PAPCNN）模型对高频系数进行融合。该方法基于参数自适应和优化连接强度，以提高性能。通过卷积稀疏表示（CSR）模型合并低频系数。实验结果表明，与现有的主流融合算法相比，该方法解决了传统 PCNN 算法中图像融合时稀疏表示参数设置困难和细节保持性差的问题，在视觉效果和客观指标上都具有显著优势。
