Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images
Tóm tắt
The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures.
Từ khóa
Tài liệu tham khảo
Goel N, Yadav A, Singh BM. Medical image processing: A review. In: Proceedings of the 2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH), Ghaziabad, India, 18–19 November 2016; pp. 57–62.
Singh P, Diwakar M, Gupta R, Kumar S, Chakraborty A, Bajal E, Paul R. A method noise-based convolutional neural network technique for CT image Denoising. Electronics. 2022;11(21):3535.
Kharrat A, Benamrane N, Messaoud MB, Abid M. Detection of brain tumor in medical images. In: Proceedings of the 2009 3rd International Conference on Signals, Circuits and Systems (SCS), Medenine, Tunisia, 6–8 November 2009; pp. 1–6.
Bian Z, Spatio-temporal Constrained Adaptive Sinogram Restoration for Low-dose Dynamic Cerebral Perfusion CT Imaging, In: 2018 IEEE Nuclear Science Symposium and Medical, Imaging Conference, Proceedings et al. (NSS/MIC), 2018, pp. 1–3, https://doi.org/10.1109/NSSMIC.2018.8824714.
Ha S, Mueller K. Low dose CT image restoration using a localized patch database, In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC), 2013, pp. 1–2, https://doi.org/10.1109/NSSMIC.2013.6829131.
Y. Zhang, J. Rong, H. Lu, Y. Xing and J. Meng, Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database, In: IEEE Transactions on Medical Imaging, vol. 36, no. 12, pp. 2510–2523, Dec. 2017, doi: 10.1109/TMI.2017.2757035.
Ma J, Algorithm-based low-dose computed tomography image reconstruction, In: Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health, Informatics et al. 2012, pp. 856–857, https://doi.org/10.1109/BHI.2012.6211721.
Wang Y et al. Noise Removal of Low-Dose CT Images Using Modified Smooth Patch Ordering, In: IEEE Access, vol. 5, pp. 26092–26103, 2017, https://doi.org/10.1109/ACCESS.2017.2777440.
Chen W et al. Low-Dose CT Image Denoising Model Based on Sparse Representation by Stationarily Classified Sub-Dictionaries, In: IEEE Access, vol. 7, pp 116859–116874, 2019, https://doi.org/10.1109/ACCESS.2019.2932754.
Hashem M, Rashed EA, Mohamed FH. Low Dose CT Image Restoration by Incremental Learning and Ant Colony Optimization, In: 2016 26th International Conference on Computer Theory and Applications (ICCTA), 2016, pp. 68–73, https://doi.org/10.1109/ICCTA40200.2016.9513236.
Tian P, Zhang W, Zhao H. ‘Intraoperative diagnosis of benign and malignant breast tissues by Fourier transform infrared spectroscopy and support vector machine classification,’’ int. J Clin Exp Med. 2015;8(1):972–81.
Wang J, Lu H, Wen J, Liang Z. Multiscale penalized weighted least-squares Sinogram Restoration for low-dose X-Ray computed Tomography. IEEE Trans Biomed Eng. 2008;55(3):1022–31. https://doi.org/10.1109/TBME.2007.909531.
Kang E, Ye JC. Framelet denoising for low-dose CT using deep learning, In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 311–314, https://doi.org/10.1109/ISBI.2018.8363581.
Huang Z et al. Deep Cascade residual networks (DCRNs): optimizing an encoder-decoder convolutional neural network for Low-Dose CT Imaging, In: IEEE transactions on Radiation and plasma Medical sciences, https://doi.org/10.1109/TRPMS.2022.3150322.
Ma J et al. Image fusion for low-dose computed tomography reconstruction, In: 2011 IEEE Nuclear Science Symposium Conference Record, 2011, pp 4239–4243, https://doi.org/10.1109/NSSMIC.2011.6153813.
Kang E, Min J, Ye JC. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017;44(10):e360e75. https://doi.org/10.1002/mp.12344.
Park HS, Baek J, You SK, Choi JK, Seo JK. Unpaired image denoising using a generative adversarial network in X-ray CT. IEEE Access. 2019;7:110414e25.
Ding Q, Chen G, Zhang X, Huang Q, Ji H, Gao H. Low-dose CT with deep learning regularization via proximal forward-backward splitting. Phys Med Biol. 2020;15(12):125009.
Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, arXiv et al. https://arxiv.org/ftp/arxiv/papers/1609/1609.08508.pdf.
Bazrafkan S, Nieuwenhove VV, Soons J, Beenhouwer JD, Sijbers J. Deep learning based computed tomography whys and wherefores. ArXiv. 2019. abs/1904.03908, https://arxiv.org/pdf/1904.03908.pdf.
Ke L, Zhang R. Multiscale Wiener filtering method for low-dose CT images, In: 2010 3rd International Conference on Biomedical Engineering and Informatics, 2010, pp. 428–431, https://doi.org/10.1109/BMEI.2010.5639560.
Liu J et al. Dec., Discriminative feature representation to improve projection data inconsistency for low dose CT imaging, In: IEEE Transactions on Medical Imaging, vol. 36, no. 12, pp. 2499–2509, 2017, https://doi.org/10.1109/TMI.2017.2739841.