CFP last date
15 May 2024
Call for Paper
June Edition
IJAIS solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 15 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Analysis of ANN Training Algorithms for Hand Geometry-Based Access Control

Kazeem B. Adedeji Apena Waliu O. Adu Michael R.
Random Articles
Reseach Article

Real-Time Medical Video Denoising with Deep Learning: Application to Angiography

by Praneeth Sadda, Taha Qarni
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 13
Year of Publication: 2018
Authors: Praneeth Sadda, Taha Qarni
10.5120/ijais2018451755

Praneeth Sadda, Taha Qarni . Real-Time Medical Video Denoising with Deep Learning: Application to Angiography. International Journal of Applied Information Systems. 12, 13 ( May 2018), 22-28. DOI=10.5120/ijais2018451755

@article{ 10.5120/ijais2018451755,
author = { Praneeth Sadda, Taha Qarni },
title = { Real-Time Medical Video Denoising with Deep Learning: Application to Angiography },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2018 },
volume = { 12 },
number = { 13 },
month = { May },
year = { 2018 },
issn = { 2249-0868 },
pages = { 22-28 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number13/1031-2018451755/ },
doi = { 10.5120/ijais2018451755 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:09:11.147705+05:30
%A Praneeth Sadda
%A Taha Qarni
%T Real-Time Medical Video Denoising with Deep Learning: Application to Angiography
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 13
%P 22-28
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.

References
  1. Borsdorf, A., Raupach R., Flohr, T., Hornegger, J. 2008. Wavelet Based Noise Reduction in CT Images Using Correlation Analysis. IEEE Transactions on Medical Imaging. 27 (Dec. 2008), 1685–1703.
  2. Macovski, A. 1996. Noise in MRI. Magnetic Resonance in Medicine. 36 (Sep. 1996), 494–497.
  3. Mateo, J. L., Fernández-Caballero, A. 2009. Finding out general tendencies in speckle noise reduction in ultrasound images. Expert Systems with Applications. 36 (May 2009), 7786–7797.
  4. Zhang, C., Helferty, J. P., McLennan G., Higgins, W. E. 2000. Nonlinear distortion correction in endoscopic video images. In Proceedings of the 2000 International Conference on Image Processing. 3 (Sep. 2003).
  5. Jolly, S. S., Amlani, S., Hamon, M., Yusuf, S., Mehta, S. R. 2009. Radial versus femoral access for coronary angiography or intervention and the impact on major bleeding and ischemic events: A systematic review and meta-analysis of randomized trials. American Heart Journal. 157 (Jan. 2009), 123–140.
  6. Patel, V. G., Brayton, K. M, Tamayo, M., Mogabgab O., Michael, T. T., Lo, N., et al. 2013. Angiographic Success and Procedural Complications in Patients Undergoing Percutaneous Coronary Chronic Total Occlusion Interventions: A Weighted Meta-Analysis of 18,061 Patients From 65 Studies. JACC Cardiovascular Intervention. 6 (Feb. 2013), 128–138.
  7. Godino, C., Colombo, A. 2015. Complications of Percutaneous Coronary Intervention. PanVascular Medicine. (Feb. 2015), 2297–2322.
  8. Grillo, T., Athayde, G., Belfort, A., Miranda, R., Beaton, A., Nascimento, B. 2015. Mitral Subvalvular Aneurysm in a Patient with Chagas Disease and Recurrent Episodes of Ventricular Tachycardia. Case Reports in Cardiology. (Nov. 2015).
  9. Kostadin, D., Foi, A., Katkovnik, V., Egiazarian, K. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing. 16 (Jul. 2007), 2080–2090
  10. Shao, L., Yan, R., Li, X., L., Y. 2013. From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms. 44 (Aug. 2013), 1001–1013.
  11. Hasan, M., M., Adaptive Edge-guided Block-matching and 3D filtering (BM3D) Image Denoising Algorithm. 2014. Thesis. University of Western Ontario. (May 2014).
  12. LeCun, Y., Bengio, Y., Hinton, G. E. 2015. Deep learning. Nature. 521 (May 2015), 436–444.
  13. Hinton, G. E., Osindero, S., Teh, Y. W. 2006. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation. 18 (Jul. 2006), 1527–1554.
  14. Hinton G. E. 2007. Learning Multiple Layers of Representation. Trends in Cognitive Sciences. 11 (Oct. 2007), 428–434.
  15. Deng L., Li J., Huang J. T., Yao K., Yu D., Seide F., et al. 2013. Recent Advances in Deep Learning for Speech Research at Microsoft. In Proceedings of the 2013 IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP). (May 2014), 8604–8608.
  16. Hinton G. E., Deng L., Yu D., Dahl G. E., Mohamed A., Jaitly N., et al. 2012. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing. 29 (Jul. 2012), 82–97.
  17. Ciresan D. C., Giusti A., Gambardella L. M., Schmidhuber J. 2013. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. Medical Image Computing and Computer-Assisted Intervention. 16-2 (Oct. 2013), 411–418.
  18. Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan T., et al. 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint (arXiv:171105225. 2017).
  19. Abràmoff M. D., Lou Y., Erginay A., Clarida W., Amelon R., Folk J. C., et al. 2016. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Ophthalmology & Visual Science. 13 (Oct. 2016), 5200.
  20. Krizhevsky, A., Sutskever, A., Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (NIPS 2012).
  21. Hinton, G. E., Salakhutdinov, R. R. 2006. Reducing the dimensionality of data with neural networks. Science. 313 (Jul. 2006), 504–507.
  22. Vincent, P., Larochelle, H., Bengio, Y., Pierre-Antoine, M. 2008. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning. 25 (Jul. 2008), 1096-1103.
  23. Honzátko, D., Kruliš, M. 2017. Accelerating block-matching and 3D filtering method for image denoising on GPUs. Journal of Real-Time Processing. 37 (Nov. 2017), 1–15.
  24. Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., Khudanpur, S. 2010. Recurrent neural network based language model. 11th Annual Conference of the International Speech Communication Association. 11 (Sep. 2010), 1045–1048.
Index Terms

Computer Science
Information Sciences

Keywords

Angiography deep learning denoising fluoroscopy machine learning neural network real-time

Powered by PhDFocusTM