Top

Published in:

2024 | OriginalPaper | Chapter

A Perspective Review of Generative Adversarial Network in Medical Image Denoising

Authors : S. P. Porkodi, V. Sarada

Published in: Micro-Electronics and Telecommunication Engineering

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This review article discusses using Generative Adversarial Networks (GANs) for image denoising in medical images. GANs have produced optimistic results in enhancing the quality of noisy medical images, which is crucial for accurate diagnosis and treatment. The article provides an overview of the challenges in medical image denoising and the working principle of GANs. The review also summarizes the recent research on using GANs for medical image denoising and compares their performance and significance. Finally, the article discusses the future directions for GAN-based medical image denoising and its potential impact on the healthcare industry.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Pioneering a New Era of Global Transactions: Decentralized Overseas Transactions on the Blockchain

next chapter Osteoporosis Detection Based on X-Ray Using Deep Convolutional Neural Network

Sagheer SVM, George SN (2020) A review on medical image denoising algorithms. Biomed Signal Process Control 61:102036CrossRef

Mondal, Maitra M (2014) Denoising and compression of medical image in wavelet 2d. Int J Recent and Innov Trends in Comput Commun 2(2):1–4

Dabov K, Foi A, Katkovnik V, Egiazarian K (2006) Image denoising with block matching and 3d filtering. In: Image processing: algorithms and systems, neural networks, and machine learning, vol 6064. SPIE, pp 354–365

Thakur RS, Chatterjee S, Yadav RN, Gupta L (2023) Medical image denoising using convolutional neural networks. In: Digital image enhancement and reconstruction, Elsevier, pp 115–138

Chai Y, Liu H, Xu J, Samtani S, Jiang Y, Liu H (2023) A multi-label classification with an adversarial-based denoising autoencoder for medical image annotation. ACM Trans Manag Inf Syst 14(2):1–21CrossRef

Gurrola-Ramos J, Dalmau O, Alarc´on TE (2021) A residual dense u-net neural network for image denoising. IEEE Access 9:31742–31754

Fan L, Zhang F, Fan H, Zhang C (2019) Brief review of image denoising techniques. Visual Comput Indus Biomed Art 2(1):1–12

Ben Hamza A, Krim H (2001) Image denoising: a nonlinear robust statistical approach. IEEE Trans Signal Process 49(12):3045–3054

Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224MathSciNetCrossRef

10.

Alkinani MH, El-Sakka MR (2017) “Patch-based models and algorithms for image denoising: A comparative review between patch-based images denoising methods for additive noise reduction.” EURASIP J Image Video Process 2017(1):1–27

11.

Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin, C.-W (2020) “Deep learning on image denoising: An overview.” Neural Netw 131:251–275

12.

Dey R, Bhattacharjee D, Nasipuri M (2020) “Image denoising using generative adversarial network.” Intell Comput: Image Process Based Appl, 73–90

13.

Porkodi SP, Sarada V, Maik V, Gurushankar, K, (2022) “Generic image application using gans (generative adversarial networks): A review.” Evol Syst, 1–15

14.

Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.” IEEE Trans Image Process 26(7):3142–3155

15.

Isola P, Zhu J.-Y, Zhou T, Efros AA (2017) “Image-to-image translation with conditional adversarial networks.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134

16.

Zhu J-Y, Park T, Isola P, Efros AA (2017) “Unpaired image-to-image trans-lation using cycle-consistent adversarial networks.” In Proceedings of the IEEE international conference on computer vision, pp. 2223–2232

17.

Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) “Improved training of wasserstein gans.” Adv Neural Inf Process Syst 30

18.

Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) “Photo-realistic single image super-resolution using a generative adversarial network.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681–4690

19.

Lample G, Zeghidour N, Usunier N, Bordes A, Denoyer L, Ranzato M (2017) “Fader networks: Manipulating images by sliding attributes.” Adv Neural Inf Process Syst 30

20.

Pascual S, Bonafonte A, Serra J, (2017) “Segan: Speech enhancement generative adversarial network.” arXiv preprint arXiv:1703.09452

21.

Yi Z, Zhang H, Tan P, Gong M (2017) “Dualgan: Unsupervised dual learning for image-to-image translation.” In Proceedings of the IEEE international conference on computer vision, pp. 2849–2857

22.

Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J (2018) “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8789–8797

23.

Huang X, Liu M-Y, Belongie S, Kautz J (2018) “Multimodal unsupervised image-to-image translation.” In Proceedings of the European conference on computer vi-sion (ECCV), pp. 172–189

24.

Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning, PMLR, pp 7354–7363

25.

Kim J, Kim M, Kang H, Lee K (2019) U-gat-it: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830

26.

Marcos L, Alirezaie J, Babyn P (2021) Low dose ct image denoising using boosting attention fusion gan with perceptual loss. In: 2021 43rd annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 3407–3410

27.

Zhang L, Zhang J (2022) Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss. PeerJ Comput Sci 8:e873CrossRef

28.

Porkodi SP, Sarada V, Maik V (2023) Dcgan for data augmentation in pneumonia chest x-ray image classification. In: Proceedings of international conference on recent trends in computing: ICRTC 2022, Springer, pp 129–137

Title: A Perspective Review of Generative Adversarial Network in Medical Image Denoising
Authors: S. P. Porkodi
V. Sarada
Publisher: Springer Nature Singapore
Book: Micro-Electronics and Telecommunication Engineering
Print ISBN: 978-981-9995-61-5

Electronic ISBN: 978-981-9995-62-2

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-9562-2_15