Medical image fusion based on sparse representation of classified image patches

Abstract: Sparse representation(SR) model named convolutional sparsity based morphological component analysis is introduced for pixel-level medical image fusion. The CS-MCA model can achieve multicomponent and global SRs of source images, by integrating MCA and convolutional sparse representation(CSR) into a unified optimization framework. In the existing method, the CSRs of its gradient and texture components are obtained by the CSMCA model using pre-learned dictionaries. Then for each image component, sparse coefficients of all the source images are merged and then fused component is reconstructed using the corresponding dictionary. In the extension mechanism, we are using deep learning based pyramid decomposition. Now a days deep learning is a very demanding technology. Deep learning is used for image classification, object detection, image segmentation, image restoration. Keywords: CNN, CT, MRI, MCA, CS-MCA.

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Medical image fusion method by using Laplacian pyramid and convolutional sparse representation

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.5632 ◽

2019 ◽

Vol 32 (17) ◽

Cited By ~ 3

Author(s):

Feiqiang Liu ◽

Lihui Chen ◽

Lu Lu ◽

Awais Ahmad ◽

Gwanggil Jeon ◽

...

Keyword(s):

Image Fusion ◽

Sparse Representation ◽

Medical Image ◽

Fusion Method ◽

Laplacian Pyramid ◽

Medical Image Fusion ◽

Image Fusion Method

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Medical Image Fusion Based on Sparse Representation and PCNN in NSCT Domain

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/2806047 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 16

Author(s):

Jingming Xia ◽

Yiming Chen ◽

Aiyue Chen ◽

Yicai Chen

Keyword(s):

Neural Network ◽

Image Fusion ◽

Sparse Representation ◽

High Frequency ◽

Medical Image ◽

Information Transfer ◽

Color Image ◽

Low Frequency ◽

Source Image ◽

Medical Image Fusion

The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of the source image. To solve these problems, a medical image fusion algorithm combined with sparse representation and pulse coupling neural network is proposed. First, the source image is decomposed into low and high frequency subband coefficients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefficients to get the overcomplete dictionary D, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefficients to complete the fusion of the low frequency subband sparse coefficients. Then, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefficients, and the fusion coefficients of the high frequency subband coefficients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. The experimental results and analysis show that the algorithm of gray and color image fusion is about 34% and 10% higher than the contrast algorithm in the edge information transfer factor QAB/F index, and the performance of the fusion result is better than the existing algorithm.

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