A novel improved deep convolutional neural network model for medical image fusion.

Autor: Xia, Kai-jian, Yin, Hong-sheng, Wang, Jiang-qiang
Předmět:
Zdroj: Cluster Computing; Jan2019 Supplement 1, Vol. 22 Issue 1, p1515-1527, 13p
Abstrakt: This paper proposed a novel fusion scheme for muti-modal medical images that utilizes both the features of the multi-scale transformation and deep convolutional neural network. Firstly, the source images are decomposed by the Gauss-Laplace filter and Gaussian filter into several sub-images in the first layer of network. Then, HeK-based method is used to initialize the convolution kernel of the rest layers, construct the basic unit, and use the back propagation algorithm to train the basic unit; Train multiple basic units that are sacked with the thought of SAE to get the deep stacked neural network; the proposed network is adopted to decompose the input images to obtain their own high frequency and low frequency images, and combine the our fusion rule to fuse the two high frequency and low frequency images, and put them back to the last layer of the network to get the final fusion images. The performance of our proposed fusion method is evaluated by conducting several experiments on the different medical image datasets. Experimental results demonstrate that our proposed method does not only produce better results by successfully fusing the different images, but also ensures an improvement in the various quantitative parameters as compared to other existing methods. In addition, the speed of our improved CNN method is much faster than that of comparison algorithms which have good fusion quality. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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