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Articles

Framework for the Restoration of Capsule Endoscopy Images Using Partial Differential Equations-based Filter

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References

  • B. Li, and M. Q. H. Meng, “Wireless capsule endoscopy images enhancement via adaptive contrast diffusion,” J. Vis. Commun. Image Represent, Vol. 23, no. 1, pp. 222–8, 2012.
  • H. Vu, T. Echigo, R. Sagawa, K. Yagi, M. Shiba, K. Higuchi, T. Arakawa, and Y. Yagi, “Detection of contractions in adaptive transit time of the small bowel from wireless capsule endoscopy videos,” Comput. Biol. Med, Vol. 39, no. 1, pp. 16–26, 2009.
  • K. K. Jani, and R. Srivastava, “A survey on medical image analysis in capsule endoscopy,” Curr. Med. Imaging Former. Curr. Med. Imaging Rev, Vol. 15, no. 7, pp. 622–36, 2018.
  • E. Spyrou, and D. K. Iakovidis, “Video-based measurements for wireless capsule endoscope tracking,” Meas. Sci. Technol, Vol. 25, no. 1, pp. 5002–5016, 2014.
  • F. Argüelles-Arias, A. Caunedo, J. Romero, A. Sánchez, M. Rodríguez-Téllez, F. J. Pellicer, F. Argüelles-Martín, and J. M. Herrerías, “The value of capsule endoscopy in pediatric patients with a suspicion of Crohn’s disease,” Endoscopy, Vol. 36, no. 10, pp. 869–73, 2004.
  • Q. Zhao, G. E. Mullin, M. Q. H. Meng, T. Dassopoulos, and R. Kumar, “A general framework for wireless capsule endoscopy study synopsis,” Comput. Med. Imaging Graph, Vol. 41, pp. 108–16, 2015.
  • N. I. R. Yassin, S. Omran, E. M. F. El Houby, and H. Allam, “Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review,” Comput. Methods Programs Biomed., Vol. 156, pp. 25–45, 2017.
  • F. Riaz, A. Hassan, R. Nisar, M. Dinis-Ribeiro, and M. T. Coimbra, “Content-adaptive region-based color texture descriptors for medical images,” IEEE J. Biomed. Heal. Inform., Vol. 21, no. 1, pp. 162–71, 2017.
  • K. Doi, “Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,” Comput. Med. Imaging Graph, Vol. 31, no. 4–5, pp. 198–211, 2007.
  • M. L. Giger, H.-P. Chan, and J. Boone, “Anniversary paper: History and status of CAD and quantitative image analysis: The role of medical physics and AAPM,” Med. Phys., Vol. 35, no. 12, pp. 5799–820, 2008.
  • A. Mishra, and K. Jani, “Comparative study on bring your own technology [BYOT]: Applications & security,” in International Conference on Electrical, Electronics, Signals, Communication and Optimization, EESCO 2015, Visakhapatnam, India, 2015.
  • K. K. Jani, S. Srivastava and R. Srivastava, “Data reduction technique for capsule endoscopy,” in Multimedia Big Data Computing for IoT Applications, K. N. Tanwar and S. Tyagi, Ed. Singapore: Springer, 2019, pp. 269–85.
  • H. Liu, W. S. Lu, and M. Q. H. Meng, “Fast algorithms for restoration of color wireless capsule endoscopy images,” Midwest Symp. Circuits Syst., Vol. 00, no. 4, pp. 11–4, 2011.
  • “Capsule image 1.” Available: https://commons.wikimedia.org/w/index.php?curid=819896 [Accessed: 6 Mar 2018].
  • “Capsule image 2.” Available: https://www.ecnmag.com/article/2012/02/reducing-size-while-improving-functionality-and-safety-next-generation-medical-device-design [Accessed 6 Mar 2018].
  • G. Liu, G. Yan, S. Kuang, and Y. Wang, “Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy,” Comput. Biol. Med., Vol. 70, pp. 131–8, 2016.
  • L. S. Fang, and H. Zhuo, “Super-resolution of hyperspectral image via superpixel-based sparse representation,” Neurocomputing, Vol. 273, pp. 171–7, 2018.
  • S. Liu, M. Liu, P. Li, J. Zhao, Z. Zhu, and X. Wang, “SAR image denoising via sparse representation in shearlet domain based on continuous cycle spinning,” IEEE Trans. Geosci. Remote Sens, Vol. 55, no. 5, pp. 2985–92, 2017.
  • M. Moradi, A. Falahati, A. Shahbahrami, and R. Zare-Hassanpour, “Improving visual quality in wireless capsule endoscopy images with contrast-limited adaptive histogram equalization,” in 2015 2nd International Conference Pattern Recognition and Image Analysis IPRIA 2015, Rasht, Iran, 2015, pp. 0–4.
  • H. Liu, W. S. Lu, and M. Q. H. Meng, “De-blurring wireless capsule endoscopy images by total variation minimization,” in Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, Canada, no. 1, 2011, pp. 102–6.
  • M. Alizadeh, A. Talebpour, H. Soltanian-Zadeh, and S. M. R. Aghamiri, “Effects of improved adaptive gamma correction method on wireless capsule endoscopy images: Illumination compensation and edge detection,” in ICEE 2012 – 20th Iranian Conference on Electrical Engineering, Tehran, Iran, 2012, pp. 1544–8.
  • R. Shahril, S. Baharun, A. K. M. M. Islam, and S. Komaki, “Anisotropic contrast diffusion enhancement using variance for wireless capsule endoscopy images,” in 2014 International Conference on Informatics, Electronics & Vision, ICIEV 2014, Dhaka, Bangladesh, 2014.
  • R. Shahri, D. Arianti, S. Baharun, A. K. M. M. Islam, and S. Komaki, “Pre-processing technique based on discrete cosine transform (DCT) and anisotropic contrast diffusion for wireless capsule endoscopy images,” in IECBES 2014, Conference Proceedings. – 2014 EEE Conference on Biomedical Engineering and Sciences “Miri, Where Eng. Med. Biol. Humanit. Meet,” Miri, Malaysia, no. December, 2015, pp. 922–927.
  • L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D Nonlinear Phenom., Vol. 60, no. 1–4, pp. 259–68, 1992.
  • Y. L. You, and M. Kaveh, “Fourth-order partial differential equations for noise removal,” IEEE Trans. Image Process, Vol. 9, no. 10, pp. 1723–30, 2000.
  • G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Meas. J. Int. Meas. Confed., Vol. 45, no. 7, pp. 1792–800, 2012.
  • J. Liu, “Research of image denoising method based on part adaptive total variation and median filter,” in International Conference on Information Science and Engineering, Nanjing, China, 2009, pp. 2699–702.
  • A. R. Jac Fredo, R. S. Abilash, and C. Suresh Kumar, “Segmentation and analysis of damages in composite images using multi-level threshold methods and geometrical features,” Meas. J. Int. Meas. Confed., Vol. 100, pp. 270–8, 2017.
  • L. H. Juang, and M. N. Wu, “Image noise reduction using Wiener filtering with pseudo-inverse,” Meas. J. Int. Meas. Confed., Vol. 43, no. 10, pp. 1649–55, 2010.
  • Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process, Vol. 13, no. 4, pp. 600–12, 2004.
  • G. Chen, C. Yang, L. Po, and S. Xie, “Edge-based structural similarity for image quality assessment,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France, vol. 2, 2006, pp. II-933–II-936.
  • A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imaging, Vol. 22, no. 3, pp. 323–31, 2003.
  • A. Anupriya, and A. Tayal, “Wavelet based image denoising using self organizing migration algorithm,” Digit. Image Process, Vol. 4, no. 10, pp. 542–6, 2012.
  • A. Chambolle, V. Caselles, M. Novaga, D. Cremers, and T. P. An, “An introduction to total variation for image analysis,” Theor. Found. Numer. Methods Sparse Recover. Gruyter, Radon Ser. Comp. Appl. Math, Vol. 9, pp. 263–340, 2009.

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