Qiu Chenhui, Wang Yuanyuan, Zhang Huan, Xia Shunren
Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University and the Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou 310027, China.
Comput Math Methods Med. 2017;2017:9308745. doi: 10.1155/2017/9308745. Epub 2017 Nov 9.
Multimodal image fusion techniques can integrate the information from different medical images to get an informative image that is more suitable for joint diagnosis, preoperative planning, intraoperative guidance, and interventional treatment. Fusing images of CT and different MR modalities are studied in this paper. Firstly, the CT and MR images are both transformed to nonsubsampled shearlet transform (NSST) domain. So the low-frequency components and high-frequency components are obtained. Then the high-frequency components are merged using the absolute-maximum rule, while the low-frequency components are merged by a sparse representation- (SR-) based approach. And the dynamic group sparsity recovery (DGSR) algorithm is proposed to improve the performance of the SR-based approach. Finally, the fused image is obtained by performing the inverse NSST on the merged components. The proposed fusion method is tested on a number of clinical CT and MR images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation.
多模态图像融合技术可以整合来自不同医学图像的信息，以获得更适合联合诊断、术前规划、术中引导和介入治疗的信息丰富的图像。本文研究了CT图像与不同磁共振成像（MR）模态图像的融合。首先，将CT图像和MR图像都变换到非下采样剪切波变换（NSST）域，从而得到低频分量和高频分量。然后，高频分量采用绝对最大值规则进行融合，而低频分量则通过基于稀疏表示（SR）的方法进行融合。并且提出了动态组稀疏恢复（DGSR）算法来提高基于SR方法的性能。最后，对融合后的分量进行NSST逆变换，得到融合图像。所提出的融合方法在一些临床CT图像和MR图像上进行了测试，并与几种流行的图像融合方法进行了比较。实验结果表明，所提出的融合方法在主观质量和客观评价方面都能提供更好的融合结果。
