Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

Medical image denoising is particularly important in brain image processing. Noise in acquisition and transmission degrades image quality and affects the reliability of diagnosis and research. Due to the complexity of the brain’s structure and minor density differences, noise can increase diagnosis difficulty, so high-quality images are essential for disease detection, prognosis assessment, and treatment plan development. This paper proposes a multi-convolutional neural network based on feature distillation learning and dense residual attention to enhance the quality of brain images and improve denoising performance. The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). Before feeding the brain images into the denoising network model, they are preprocessed using a modified watershed algorithm based on a combination of a morphological gradient, Sobel’s operator, and Canny’s operator. The GSN is used to extract global features and increase the sensory field, and the DRAN efficiently extracts key features by combining improved channel attention and spatial attention mechanisms. The FDN extracts useful features through two feature distillation blocks, suppresses redundant information, and reduces computational complexity. The FPB performs feature fusion. Experimental results on brain image datasets and ground-based open datasets show that the proposed model outperforms existing methods in several metrics, and helps to improve the accuracy of brain disease diagnosis and treatment.

Keywords

watershed algorithm / dense residual attention network / feature distillation network / multi-convolutional neural network / brain image denoising

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] P. S. Chaitanya, S. K. Satpathy, A multilevel de-noising approach for precision edge- based fragmentation in MRI brain tumor segmentation, Trait. Signal, 40 (2023), 1715-1722. https://doi.org/10.18280/ts.400440

[2] K. Gong, K. Johnson, G. El Fakhri, Q. Li, T. Pan, PET image denoising based on denoising diffusion probabilistic model, Eur. J. Nucl. Med. Mol. Imaging, 51 (2024), 358-368. https://doi.org/10.1007/s00259-023-06417-8

[3] H. Aetesam, S. K. Maji, Deep variational magnetic resonance image denoising via network conditioning, Biomed. Signal Process. Control, 95 (2024), 106452. https://doi.org/10.1016/j.bspc.2024.106452

[4] A. Sharma, V. Chaurasia, MRI denoising using advanced NLM filtering with non- subsampled shearlet transform, Signal Image Video Process., 15 (2021), 1331-1339. https://doi.org/10.1007/s11760-021-01864-y

[5] S. V. M. Sagheer, S. N. George, A review on medical image denoising algorithms, Biomed. Signal Process. Control, 61 (2020), 102036. https://doi.org/10.1016/j.bspc.2020.102036

[6] J. Zhang, W. Gong, L. Ye, F. Wang, Z. Shangguan, Y. Cheng, A review of deep learning methods for denoising of medical low-dose CT images, Comput. Biol. Med., 171 (2024), 108112. https://doi.org/10.1016/j.compbiomed.2024.108112

[7] J. Pan, Q. Zuo, B. Wang, C. L. P. Chen, B. Lei, S. Wang, DecGAN: Decoupling generative adversarial network for detecting abnormal neural circuits in Alzheimer’s disease, IEEE Trans. Artif. Intell., 5 (2024), 5050-5063. https://doi.org/10.1109/TAI.2024.3416420

[8] M. Li, R. Idoughi, B. Choudhury, W. Heidrich, Statistical model for OCT image denoising, Biomed. Opt. Express, 8 (2017), 3903-3917. https://doi.org/10.1364/BOE.8.003903

[9] S. Dolui, A. Kuurstra, I. C. S. Patarroyo, O.V. Michailovich, A new similarity measure for non- local means filtering of MRI images, J. Visual Commun. Image Representation, 24 (2013), 1040-1054. https://doi.org/10.1016/j.jvcir.2013.06.011

[10] C. Swarup, K. U. Singh, A. Kumar, S. K. Pandey, N. Varshney, T. Singh, Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches, Electron. Res. Arch., 31 (2023), 2900-2924. https://doi.org/10.3934/era.2023146

[11] K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 26 (2017), 3142-3155. https://doi.org/10.1109/TIP.2017.2662206

[12] C. Tian, Y. Xu, L. Fei, J. Wang, J. Wen, N. Luo, Enhanced CNN for image denoising, CAAI Trans. Intell. Technol., 4 (2019), 17-23. https://doi.org/10.1049/trit.2018.1054

[13] K. Zhang, W. Zuo, L. Zhang, FFDNet: Toward a fast and flexible solution for CNN-based image denoising, IEEE Trans. Image Process., 27 (2018), 4608-4622. https://doi.org/10.1109/TIP.2018.2839891

[14] W. Jifara, F. Jiang, S. Rho, M. Cheng, S. Liu, Medical image denoising using convolutional neural network: A residual learning approach, J. Supercomput., 75 (2019), 704-718. https://doi.org/10.1007/s11227-017-2080-0

[15] C. Tian, Y. Xu, W. Zuo, Image denoising using deep CNN with batch renormalization, Neural Networks, 121 (2020), 461-473. https://doi.org/10.1016/j.neunet.2019.08.022

[16] M. S. Hema, Sowjanya, N. Sharma, G. Abhishek, G. Shivani, P. P. Kumar, Identification and classification of brain tumor using convolutional neural network with autoencoder feature selection, in International Conference on Emerging Technologies in Computer Engineering, 1591 (2022), 251-258. https://doi.org/10.1007/978-3-031-07012-922

[17] C. Tian, M. Zheng, W. Zuo, B. Zhang, Y. Zhang, D. Zhang, Multi-stage image denoising with the wavelet transform, Pattern Recognit., 134 (2023), 109050. https://doi.org/10.1016/j.patcog.2022.109050

[18] T. Li, Z. Zhang, M. Zhu, Z. Cui, D. Wei, Combining transformer global and local feature extraction for object detection, Complex Intell. Syst., 10 (2024), 4897-4920. https://doi.org/10.1007/s40747-024-01409-z

[19] Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, Q. Hu, ECA-Net: Efficient channel attention for deep convolutional neural networks, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), 11531-11539. https://doi.org/10.1109/CVPR42600.2020.01155

[20] D. Hong, C. Huang, C. Yang, J. Li, Y. Qian, C. Cai, FFA-DMRI: A network based on feature fusion and attention mechanism for brain MRI denoising, Front. Neurosci., 14 (2020), 577937. https://doi.org/10.3389/fnins.2020.577937

[21] Y. Chen, R. Xia, K. Yang, K. Zou, MFMAM: Image inpainting via multi-scale feature module with attention module, Comput. Vision Image Understanding, 238 (2024), 103883. https://doi.org/10.1016/j.cviu.2023.103883

[22] J. Deng, C. Hu, Recovering a clean background: A new progressive multi-scale CNN for image denoising, Signal Image Video Process., 18 (2024), 4541-4552. https://doi.org/10.1007/s11760-024-03093-5

[23] M. Duong, B. N. Thi, S. Lee, M. Hong, Multi-branch network for color image denoising using dilated convolution and attention mechanisms, Sensors, 24 (2024), 3608. https://doi.org/10.3390/s24113608

[24] Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image restoration, IEEE Trans. Pattern Anal. Mach. Intell., 43 (2021), 2480-2495. https://doi.org/10.1109/TPAMI.2020.2968521

[25] F. Gao, Y. Wang, Z. Yang, Y. Ma, Q. Zhang, Single image super-resolution based on multi-scale dense attention network, Soft Comput., 27 (2023), 2981-2992. https://doi.org/10.1007/s00500-022-07456-3

[26] Y. Li, T. Xie, D. Mei, Application of convolutional neural networks for parallel multi- scale feature extraction in noise image denoising, IEEE Access, 12 (2024), 98599-98610. https://doi.org/10.1109/ACCESS.2024.3427143

[27] Y. Zhang, C. Wang, X. Lv, Y. Song, Attention-driven residual-dense network for no- reference image quality assessment, Signal Image Video Process., 18 (2024), 537-551. https://doi.org/10.1007/s11760-024-03172-7

[28] Z. Hui, X. Wang, X. Gao, Fast and accurate single image super-resolution via information distillation network, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 723-731. https://doi.org/10.1109/CVPR.2018.00082

[29] J. Liu, J. Tang, G. Wu, Residual feature distillation network for lightweight image super-resolution, in European Conference on Computer Vision, 12537 (2020), 41-55. https://doi.org/10.1007/978-3-030-67070-22

[30] Y. Zhang, Y. Liu, Q. Li, J. Wang, M. Qi, H. Sun, et al., A lightweight fusion distillation network for image deblurring and deraining, Sensors, 21 (2021), 5312. https://doi.org/10.3390/s21165312

[31] Z. Zong, L. Zha, J. Jiang, X. Liu, Asymmetric information distillation network for lightweight super resolution, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2022), 1248-1257. https://doi.org/10.1109/CVPRW56347.2022.00131

[32] Z. Yu, K. Xie, C. Wen, J. He, W. Zhang, A lightweight image super-resolution reconstruction algorithm based on the residual feature distillation mechanism, Sensors, 24 (2024), 1049. https://doi.org/10.3390/s24041049

[33] M. Jiang, C. You, M. Wang, H. Zhang, Z. Gao, D. Wu, et al., Controllable deep learning denoising model for ultrasound images using synthetic noisy image, in Computer Graphics International Conference, 14495 (2024), 297-308. https://doi.org/10.1007/978-3-031-50069-525

[34] W. Wu, S. Liu, Y. Xia, Y. Zhang, Dual residual attention network for image denoising, Pattern Recognit., 149 (2024), 110291. https://doi.org/10.1016/j.patcog.2024.110291

[35] W. Wu, G. Lv, S. Liao, Y. Zhang, FEUNet: A flexible and effective U-shaped network for image denoising, Signal Image Video Process., 17 (2023), 2545-2553. https://doi.org/10.1007/s11760-022-02471-1

[36] R. K. Thakur, S. K. Maji, Multi scale pixel attention and feature extraction based neural network for image denoising, Pattern Recognit., 141 (2023), 109603. https://doi.org/10.1016/j.patcog.2023.109603

[37] C. Tian, Y. Xu, W. Zuo, B. Du, C. Lin, D. Zhang, Designing and training of a dual CNN for image denoising, Knowl. Based Syst., 226 (2021), 106949. https://doi.org/10.1016/j.knosys.2021.106949

[38] J. Yang, H. Xie, N. Xue, A. Zhang, Research on underwater image denoising based on dual-channel residual network, Comput. Eng., 49 (2023), 188-198. https://doi.org/10.19678/j.issn.1000-3428.0064662

[39] S. Ghaderi, S. Mohammadi, K. Ghaderi, F. Kiasat, M. Mohammadi, Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series, Annal. Med. Surg., 86 (2024), 1460-1475. https://doi.org/10.1097/MS9.0000000000001756

[40] V. Sivakumar, N. Janakiraman, A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA, Biosystems, 198 (2020), 104226. https://doi.org/10.1016/j.biosystems.2020.104226

[41] Y. Liang, J. Fu, Watershed algorithm for medical image segmentation based on morphology and total variation model, Int. J. Pattern Recognit Artif Intell., 33 (2019), 1954019. https://doi.org/10.1142/S0218001419540193

[42] Y. Wu, Q. Li, The algorithm of watershed color image segmentation based on morphological gradient, Sensors, 22 (2022), 8202. https://doi.org/10.3390/s22218202

[43] T. Yesmin, H. Lohiya, P. P. Acharjya, Detection and segmentation of brain tumor by using modified watershed algorithm and thresholding to reduce over-segmentation, in 2023 IEEE International Conference on Contemporary Computing and Communications (InC4), (2023), 1-6. https://doi.org/10.1109/InC457730.2023.10262891

[44] X. Xu, S. Xu, L. Jin, E. Song, Characteristic analysis of Otsu threshold and its applications, Pattern Recognit. Lett., 32 (2011), 956-961. https://doi.org/10.1016/j.patrec.2011.01.021

[45] C. Kumari, A. Mustafi, A novel radial kernel watershed basis segmentation algorithm for color image segmentation, Wireless Pers. Commun., 133 (2023), 2105-2124. https://doi.org/10.1007/s11277-023-10831-4

[46] C. Zhang, J. Fang, Edge detection based on improved sobel operator, in Proceedings of the 2016 International Conference on Computer Engineering and Information Systems , (2016), 129-132. https://doi.org/10.2991/ceis-16.2016.25

[47] B. Chen, W. Wu, Z. Li, T. Han, Z. Chen, W. Zhang, Attention-guided cross-modal multiple feature aggregation network for RGB-D salient object detection, Electron. Res. Arch., 32 (2024), 643-669. https://doi.org/10.3934/era.2024031

[48] Z. Cai, L. Xu, J. Zhang, Y. Feng, L. Zhu, F. Liu, ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention, Electron. Res. Arch., 32 (2024), 6698-6716. https://doi.org/10.3934/era.2024313

[49] Y. Chen, R. Xia, K. Yang, K. Zou, DARGS: Image inpainting algorithm via deep attention residuals group and semantics, J. King Saud Univ. Comput. Inf. Sci., 35 (2023), 101567. https://doi.org/10.1016/j.jksuci.2023.101567

[50] J. Yang, F. Xie, H. Fan, Z. Jiang, J. Liu, Classification for dermoscopy images using convolutional neural networks based on region average pooling, IEEE Access, 6 (2018), 65130-65138. https://doi.org/10.1109/ACCESS.2018.2877587

[51] H. Wu, X. Gu, Max-pooling dropout for regularization of convolutional neural networks, in International Conference on Neural Information Processing, 9489 (2015), 46-54. https://doi.org/10.1007/978-3-319-26532-26

[52] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770-778. https://doi.org/10.1109/CVPR.2016.90

[53] G. Huang, Z. Liu, L. V. D. Maaten, K. Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 2261-2269. https://doi.org/10.1109/CVPR.2017.243

[54] Z. Feng, Y. Chen, L. Xie, Unsupervised anomaly detection via knowledge distillation with non-directly-coupled student block fusion, Mach. Vision Appl., 34 (2023), 104. https://doi.org/10.1007/s00138-023-01454-7

[55] X. Zhao, X. Cai, Y. Xue, Y. Liao, L. Lin, T. Zhao, UKD-Net: efficient image enhancement with knowledge distillation, J. Electron. Imaging, 33 (2024), 023024. https://doi.org/10.1117/1.JEI.33.2.023024

[56] Y. Li, J. Cao, Z. Li, S. Oh, N. Komuro, Lightweight single image super-resolution with dense connection distillation network, ACM Trans. Multimedia Comput. Commun. Appl., 17 (2021), 1-17. https://doi.org/10.1145/3414838

[57] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, preprint, arXiv:1412.6980.

[58] K. A. Johnson, J. A. Becker, The Whole Brain Atlas, 2005. Available from: https://www.med.harvard.edu/aanlib/home.html.

[59] S. Roth, M. J. Black, Fields of experts: A framework for learning image priors, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2 (2005), 860-867. https://doi.org/10.1109/CVPR.2005.160

[60] True Color Kodak Images, Kodak Lossless True Color Image Suite: PhotoCD PCD0992, 2013. Available from: https://r0k.us/graphics/kodak/.

[61] A. Abdelhamed, S. Lin, M. S. Brown, A high-quality denoising dataset for smartphone cameras, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 1692-1700. https://doi.org/10.1109/CVPR.2018.00182

[62] M. Lebrun, M. Colom, J. Morel, The noise clinic: A blind image denoising algorithm, Image Process. On Line, 5 (2015), 1-54. https://doi.org/10.5201/ipol.2015.125