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Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network

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Abstract

We present a registration and fusion method of fluorescein fundus angiography image and color fundus image which combines Nonsubsampled Contourlet (NSCT) and adaptive Pulse Coupled Neural Network (PCNN). Firstly, we register two images by Speeded Up Robust Features (SURF) feature points, the nearest neighbor and the next nearest neighbor distance ratio method to eliminate the spatial difference between the source images. Secondly, we use Random Sample Consensus (RANSAC) algorithm to achieve precise matching of feature points. Then, according to the transformation parameters obtained by RANSAC algorithm, we perform spatial transformation on the floating image to complete the registration. Finally, we obtain the low-frequency sub-band and high-frequency sub-band of the image to be fused by NSCT decomposition. The low-frequency sub-band is fused by the regional energy. The high-frequency sub-bands are studied using a simplified-PCNN model and the Particle Swarm Optimization algorithm. The link strength of the simplified-PCNN is an improved Laplacian energy and the images are fused based on the number of times the pixels are ignited. The proposed method has higher average gradient (AG) value and information entropy (IE) value and lower relative global dimensional synthesis error (ERGAS) than the existing fusion methods of the fundus image. The fusion image can accurately synthesize the image information, clarify the performance of the details, and has better spectral quality in the spectral range. The image of fused provides an effective reference for the clinical diagnosis of fundus diseases.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China, under grant No. 61771340; Tianjin Science and Technology Major Projects and Engineering, under grant No. 17ZXHLSY00040, No. 17ZXSCSY00060 and No. 17ZXSCSY00090.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China

    Jun Wu, Xingxing Ren, Zhitao Xiao, Fang Zhang, Lei Geng & Shihao Zhang

  2. Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China

    Jun Wu, Xingxing Ren, Zhitao Xiao, Fang Zhang, Lei Geng & Shihao Zhang

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  1. Jun Wu
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  2. Xingxing Ren
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  3. Zhitao Xiao
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  4. Fang Zhang
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  6. Shihao Zhang
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Correspondence to Zhitao Xiao.

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Preliminary results of this work were presented at the 3rd International Symposium on Artificial Intelligence and Robotics (ISAIR) 2018, Nanjing, China.

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Wu, J., Ren, X., Xiao, Z. et al. Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network. Multimed Tools Appl 79, 34795–34812 (2020). https://doi.org/10.1007/s11042-019-08194-9

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  • DOI: https://doi.org/10.1007/s11042-019-08194-9

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