New hybrid Filtering Methods to remove noise from Digital Images Article's
Main Article Content
Abstract
Using hybrid digital filtering techniques to reduce the amount of time a patient is exposed to radiation. If the patient is exposed to radiation for an extended length of time, his life will be jeopardized. Noise reduction in the photos is necessary when asked to lower the high radiation dosage and time it takes, which hides some of the right information from the physician and makes it difficult for him to appropriately analyze it. As a consequence, pre-processing has become the most significant item in digital image processors, and in this research, we offer a novel hybrid technology approach to decrease noise caused when photos are captured with medical equipment or when they are transferred. While respecting the image's edges, the resulting noise of the salt and pepper type. The statistical quantity measurements, Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) are used to assess the enhanced picture quality. To illustrate this method, take 0.08 salts and pepper noise and compute it as MSE it becomes 2.58 and PSNR 202.67, which indicates that this method is better than others at removing higher noise as well.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
K. Belattar, S. Mostefai, and A. Draa, "Intelligent content-based dermoscopic image retrieval with relevance feedback for computer-aided melanoma diagnosis," Journal of Information Technology Research (JITR), vol. 10, no. 1, pp. 85-108, 2017.
P. R. Chanu and K. Singh, "Impulse noise removal from medical images by two stage quaternion vector median filter," Journal of medical systems, vol. 42, no. 10, pp. 1-10, 2018.
P. Gravel, G. Beaudoin, and J. A. De Guise, "A method for modeling noise in medical images," IEEE Transactions on medical imaging, vol. 23, no. 10, pp. 1221-1232, 2004.
Z. HosseinKhani et al., "Adaptive real-time removal of impulse noise in medical images," Journal of medical systems, vol. 42, no. 11, pp. 1-9, 2018.
F. Russo and G. Ramponi, "A fuzzy filter for images corrupted by impulse noise," IEEE Signal Processing Letters, vol. 3, no. 6, pp. 168-170, 1996.
K. Anisha and M. Wilscy, "Impulse noise removal from medical images using fuzzy genetic algorithm," The International Journal of Multimedia & Its Applications, vol. 3, no. 4, p. 93, 2011.
Y. B. Rytsar and I. B. Ivasenko, "Application of (alpha, beta)-trimmed mean filtering for removal of additive noise from images," in Current Ukrainian Research in Optics and Photonics: Optoelectronic and Hybrid Optical/Digital Systems for Image Processing, 1997, vol. 3238: SPIE, pp. 45-52.
V. Saradhadevi and V. Sundaram, "A novel two-stage impulse noise removal technique based on neural networks and fuzzy decision," International Journal of Computer Applications, vol. 21, no. 3, pp. 31-42, 2011.
E. S. Hore, B. Qiu, and H. R. Wu, "Improved vector filtering for color images using fuzzy noise detection," Optical Engineering, vol. 42, no. 6, pp. 1656-1664, 2003.
C. Zhao, Q. Xing, and W. Nie, "Noise attenuation characteristics of generalized morphological filters," in International Symposium on Multispectral Image Processing (ISMIP'98), 1998, vol. 3545: SPIE, pp. 236-239.
C. Van Leeuwen et al., "The alfa and beta of tumours: a review of parameters of the linear-quadratic model, derived from clinical radiotherapy studies," Radiation oncology, vol. 13, no. 1, pp. 1-11, 2018.
R. C. Gonzalez and R. E. Woods, "Digital image processing, global edition," Digital Image Processing, Global Edition, vol. 19, 2018.
A. Javed, Y.-C. Kim, M. C. Khoo, S. L. D. Ward, and K. S. Nayak, "Dynamic 3-D MR visualization and detection of upper airway obstruction during sleep using region-growing segmentation," IEEE Transactions on Biomedical Engineering, vol. 63, no. 2, pp. 431-437, 2015.
A. A. Malek, W. E. Z. W. A. Rahman, S. S. Yasiran, A. K. Jumaat, and U. M. A. Jalil, "Seed point selection for seed-based region growing in segmenting microcalcifications," in 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE), 2012: IEEE, pp. 1-5.
R. C. Gonzalez, Digital image processing. Pearson education india, 2009.
J. Tukey, "Nonlinear (nonsuperposable) methods for smoothing data, in" Cong," ed: Rec, 1974.
S. V. Kumar, "Hybrid filters for medical image reconstruction," African Journal of Mathematics and Computer Science Research, vol. 6, no. 9, pp. 177-182, 2013.
M. A. Aslam, M. A. Munir, and D. Cui, "Noise removal from medical images using hybrid filters of technique," in Journal of Physics: Conference Series, 2020, vol. 1518, no. 1: IOP Publishing, p. 012061.