Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

By Ming Yin, Xiaoning Liu, Yu Liu, Xun Chen. IEEE Transactions on Instrumentation and Measurement, 2019

487 citations recorded by the source corpus.

As an effective way to integrate the information contained in multiple medical images with different modalities, medical image fusion has emerged as a powerful technique in various clinical applications such as disease diagnosis and treatment planning. In this paper, a new multimodal medical image fusion method in nonsubsampled shearlet transform (NSST) domain is proposed. In the proposed method, the NSST decomposition is first performed on the source images to obtain their multiscale and multidirection representations. The high-frequency bands are fused by a parameter-adaptive pulse-coupled neural network (PA-PCNN) model, in which all the PCNN parameters can be adaptively estimated by the input band. The low-frequency bands are merged by a novel strategy that simultaneously addresses two crucial issues in medical image fusion, namely, energy preservation and detail extraction. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency bands. The effectiveness of the proposed method is verified by four different categories of medical image fusion problems [computed tomography (CT) and magnetic resonance (MR), MR-T1 and MR-T2, MR and positron emission tomography, and MR and single-photon emission CT] with more than 80 pairs of source images in total. Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/1a494ab86d99aeea8df5047132adbdc0f90eddd6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Hao Zhang
3. Han Xu
4. Zhenfeng Shao
5. Xiaojie Guo
6. Junjun Jiang
7. Pengwei Liang
8. Linfeng Tang

In Media Technology

1. Jiayi Ma
2. Hao Zhang
3. Han Xu
4. Zhenfeng Shao
5. Xiaojie Guo
6. Yiannis Demiris
7. Xingchen Zhang
8. Linfeng Tang

Citations in the corpus

• Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity (2020, methodology): “In medical image fusion, PA-PCNN proposed by Yin et al. (Yin et al. 2018) can realize the fusion of different types of medical images, which is a novel method in nonsubsampled shearlet transform (NSST) domain.”
• GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, background): “Similarly, Yin et al. [36] designed an interesting neural network named PA-PCNN to fuse the high-frequency bands of medical images, which performs well in four medical image fusion tasks, including computed tomography (CT) and magnetic resonance (MR), MR-T1 and MR-T2, MR and positron emission…”
• U2Fusion: A Unified Unsupervised Image Fusion Network (2020, methodology): “We compare U2Fusion with RPCNN [44], CNN [16], PAPCNN [45], and NSCT [6] on the Harvard dataset.”