Abstract
MRI imaging is one of the most widespread techniques in biomedical applications. Impulse noise is frequently a problem affecting the diagnosis of the disease, and it's a challenging task to denoise. However, the standard linear filter cannot detect the distribution of noise accurately due to the extremely nonlinear characteristic of the impulse noise with the one-dimensional measures to describe the behavior of pixels. The paper proposes to find out the relationship between multiple measures by Cartesian Genetic Programming to comprehensively describe the behavior of pixels from multiple dimensions to increase the robustness of detection results, and more complementary feature detectors are constructed by using the multi-gene output characteristics of the model. In the recovery stage, an adaptive median filter and edge-preserving filter (AMEPF) will be proposed which consists of three-layer filters to enhance the image and reduces the traditional over-dependence on the result of the detection phase, the filter can eliminate noise and protect the integrity of the structure. Different experimental results show that the recovery effect under different impulse noise intensity is better than the previous methods.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61977018), and the Natural Science Foundation of Guangdong Province, China (Grant No. 2015A030313501).
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Huang, W., He, P., Yan, Z., Wu, H. (2022). An Efficient MRI Impulse Noise Multi-stage Hybrid Filter Based on Cartesian Genetic Programming. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-89698-0_11
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DOI: https://doi.org/10.1007/978-3-030-89698-0_11
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