Ultrasound Images Resolution Enhancement using Generative Adversarial Network

Jarif, Afeef Ahmed; Md Intiser Ali, Chowdhury; Chowdhury, Muhammad Jawad

dc.contributor.author	Jarif, Afeef Ahmed
dc.contributor.author	Md Intiser Ali, Chowdhury
dc.contributor.author	Chowdhury, Muhammad Jawad
dc.date.accessioned	2024-08-28T10:06:09Z
dc.date.available	2024-08-28T10:06:09Z
dc.date.issued	2023-05-30
dc.identifier.citation	[1] Patil, Rajesh Bhosale, Surendra. (2022). Medical Image Denoising Techniques: A Review. 4. 21-33. 10.46328/ijonest.76. [2] Y. Liang, J. Wang, S. Zhou, Y. Gong, and N. Zheng, “Incorporating image priors with deep convolutional neural networks for image super-resolution,” Neurocomputing, vol. 194, pp. 340–347, 2016. [3] H. Liu, Z. Fu, J. Han, L. Shao, S. Hou, and Y. Chu, “Single image super resolution using multi-scale deep encoder–decoder with phase congruency edge map guidance,” Information Sciences, vol. 473, pp. 44–58, 2019. [4] Z. Wang, P. Yi, K. Jiang, J. Jiang, Z. Han, T. Lu, and J. Ma, “Multi-memory convolutional neural network for video super-resolution,” IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2530–2544, 2019. [5] J. Ma, X. Wang, and J. Jiang, “Image superresolution via dense discriminative network,” IEEE Transactions on Industrial Electronics, vol. 67, no. 7, pp. 5687–5695, 2020. [6] N. Zhao, Q. Wei, A. Basarab, D. Kouam´e, and J.-Y. Tourneret, “Single image super-resolution of medical ultrasound images using a fast algorithm,” in Proc. of the IEEE 13th Int. Symposium on Biomedical Imaging. IEEE, 2016, pp. 473–476. [7] K. Diamantis, A. H. Greenaway, T. Anderson, J. A. Jensen, P. A. Dalgarno, and V. Sboros, “Super-resolution axial localization of ultrasound scatter using multi-focal imaging,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 8, pp. 1840–1851, 2018. [8] K. Umehara, J. Ota, and T. Ishida, “Application of superresolution convolu tional neural network for enhancing image resolution in chest ct,” Journal of digital imaging, vol. 31, no. 4, pp. 441–450, 2018. 40 [9] A. Shocher, N. Cohen, and M. Irani, ““zero-shot” superresolution using deep internal learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3118–3126. [10] J. Lu and W. Liu, “Unsupervised super-resolution framework for medical ultrasound images using dilated convolutional neural networks,” in Proc. of the IEEE 3rd Int. Conf. on Image, Vision and Computing. IEEE, 2018, pp. 739–744. [11] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). [12] C. Ledig et al., “Photo-Realistic Single Image Super-Resolution Using a Gen erative Adversarial Network,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 105-114, doi: 10.1109/CVPR.2017.19. [13] Wang, Xintao Yu, Ke Wu, Shixiang Gu, Jinjin Liu, Yihao Dong, Chao Loy, Chen Change Qiao, Yu Tang, Xiaoou. (2018). ESRGAN: Enhanced Super Resolution Generative Adversarial Networks. [14] Zhao, M., Wei, Y., Wong, K. K. (2022). A generative adversarial network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images. Magnetic Resonance Imaging, 85, 153-160. [15] S´anchez, I., Vilaplana, V. (2018). Brain MRI super-resolution using 3D gen erative adversarial networks. arXiv preprint arXiv:1812.11440. [16] Zhang, L., Zhang, J. (2022). Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss. PeerJ Computer Science, 8, e873. [17] Maeda, S. (2020). Unpaired image super-resolution using pseudo-supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 291-300) 41 [18] Liu, H., Liu, J., Hou, S., Tao, T., Han, J. (2021). Perception consistency ultrasound image super-resolution via self-supervised CycleGAN. Neural Com puting and Applications, 1-11. [19] C. Dong, C. C. Loy, K. He, and X. Tang, “Image superresolution using deep convolutional networks,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 2, pp. 295–307, 2016. [20] Xia, P., Tahara, T., Kakue, T. et al. Performance comparison of bi linear interpolation, bicubic interpolation, and B-spline interpolation in parallel phase-shifting digital holography. OPT REV 20, 193–197 (2013). https://doi.org/10.1007/s10043-013-0033-2 [21] Zhang, Saiping Yang, Fuzheng Wan, Shuai Di, Peiyun. (2020). Spher ical Lanczos Interpolation in Planar Projection or Format Conversions of Panoramic Videos. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2964789. [22] A. Hor´e and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, pp. 2366-2369, doi: 10.1109/ICPR.2010.579. [23] Xu, Bing Wang, Naiyan Chen, Tianqi Li, Mu. (2015). Empirical Evaluation of Rectified Activations in Convolutional Network. [24] H. R. Sheikh, A. C. Bovik and G. de Veciana, “An information fidelity cri terion for image quality assessment using natural scene statistics,” in IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2117-2128, Dec. 2005, doi: 10.1109/TIP.2005.859389.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2137
dc.description	Supervised by Mr. Tareque Mohmud Chowdhury, Assistant Professor, CoSupervisor, Tasnim Ahmed, Lecturer, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	This thesis focuses on enhancing ultrasound scan resolution and image quality, particularly for the detection of breast cancer. During pregnancy, diagnostic ultrasound is frequently used to examine internal organs and track fetal progress. The presence of noise in ultrasonic imaging, however, makes diagnosis and interpretation difficult. A generative adversarial network (GAN) is suggested as a deep learning method to address this problem. The GAN is made up of a generator network that has been trained to transform low-resolution ultrasonic in puts into high-resolution outputs and a discriminator network that can tell real images from fake ones. Convolutional layers, skip connections, Multi resolution Convolution blocks (MRCB), and loss functions are all incor porated into the GAN model’s architectural choices, which are tailored to the peculiarities of breast cancer. For iterative optimization throughout the training phase, backpropagation and gradient descent techniques are used. The peak signal-to-noise ratio (PSNR) and structural similarity in dex (SSIM) are quantitative metrics that are used to assess the effectiveness of the proposed Ultrasound Images Resolution Enhancement GAN (UIRE GAN) approach. The findings show considerable increases in image quality, increasing the capability of breast cancer ultrasound imaging as a diagnostic tool. This study advances the area of breast cancer imaging and has the potential to help patients receive better healthcare.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Ultrasound Images Resolution Enhancement using Generative Adversarial Network	en_US
dc.type	Thesis	en_US