Speckle Noise Reduction and Image Segmentation Based on a Modified Mean Filter

P. Arulpandy; M. Trinita Pricilla

doi:10.24423/cames.290

P. Arulpandy Bannari Amman Institute of Technology
M. Trinita Pricilla Nirmala College for Women

DOI: http://dx.doi.org/10.24423/cames.290

Abstract

Image segmentation is an essential process in many fields involving digital images. In general, segmentation is the process of dividing the image into objects and background image. Image segmentation is an important step in the object detection process. It becomes more critical if a given image is corrupted by noise. Most digital images are corrupted by noises such as salt and pepper noise, Gaussian noise, Poisson noise, speckle noise, etc. Speckle noise is a multiplicative noise that affects pixels in a gray-scale image, and mainly occurs in low level luminance images such as Synthetic Aperture Radar (SAR) images and Magnetic Resonance Image (MRI) images. Image enhancement is an essential task to reduce specklenoise prior to performing further image processing such as object detection, image segmentation, edge detection, etc. Here, we propose a neighborhood-based algorithm to reduce speckle noise in gray-scale images. The main aim of the noise reduction technique is to segment the noisy image. So that the proposed algorithm applies some luminance to the original image. The proposed technique performs well at maximum noise variance. Finally, the segmentation process is done by the modified mean filter. The proposed technique has three phases. In phase 1, the speckle noise is reduced and the contrast adjustment is made. In phase 2, the segmentation of the enhanced image is processed. Finally, in phase 3, the isolated pixels in the segmented image are eliminated and the final segmented image is generated. This technique does not require any threshold value to segment the image; it will be automatically calculated based on the mean value.

Keywords

Image segmentation, Speckle noise, Image analysis, Image denoising, Neighborhood segmentation,

References

1. A. Rosenfeld, P. de la Torre, Histogram concavity analysis as an aid in threshold selection, IEEE Transactions on Systems Man and Cybernetics, 13(2): 231–235, 1983, doi: 10.1109/TSMC.1983.6313118.
2. J.S. Weszka, A. Rosenfeld, Histogram modification for threshold selection, IEEE Transactions on Systems Man and Cybernetics, 9(1): 38–52, 1979, doi: 10.1109/TSMC.1979.4310072.
3. M. Zhang, L. Zhang, H.D. Cheng, A neutrosophic approach to image segmentation based on watershed method, Signal Processing, 90(5): 1510–1517, 2010, doi: 10.1016/j.sigpro.2009.10.021.
4. M.N. Qureshi, M.V. Ahamad, An improved method for image segmentation using k-means clustering with neutrosophic logic, Procedia Computer Science, 132(7): 534–540, 2018, doi: 0.1016/j.procs.2018.05.006.
5. H.D. Cheng, Y. Guo, Y. Zhang, A novel image segmentation approach based on neutrosophic set and improved fuzzy c-means algorithm, Neural Mathematics and Natural Computation, 7(1): 155–171, 2011, doi: 10.1142/S1793005711001858.
6. N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62–66, 1979, doi: 10.1109/TSMC.1979.4310076.
7. J.-M. Sung, D.-C. Kim, Y.-H. Ha, Image thresholding using standard deviation, Proceeding SPIE: Image Processing: Machine Vision Applications VII, 90240R, 2014, doi: 10.1117/12.2040990.
8. N. Sang, H. Li, W. Peng, T. Zhang, Knowledge-based adaptive thresholding segmentation of digital subtraction angiography images, Image and Vision Computing, 25(8): 1263–1270, 2007, doi: 10.1016/j.imavis.2006.07.026.
9. P.L. Rosin, E. Ioannidis, Evaluation of global image thresholding for change detection, Pattern Recognition Letters, 24(14): 2345–2356, 2003, doi: 10.1016/S0167-8655(03)00060-6.
10. Z. Li, Y. Cheng, C. Liu, C. Zhao, Minimum standard deviation difference-based thresholding, International Conference on Measuring Technology and Mechatronics Automation, 2: 664–667, 2010, doi: 10.1109/ICMTMA.2010.579.
11. Z. Hou, Q. Hu, W. Nowinski, On minimum variance thresholding, Pattern Recognition Letters, 27(14): 1732–1743, 2006, doi: 10.1016/j.patrec.2006.04.012.
12. S. Gai, B. Zhang, C. Yang, L. Yu, Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution,Digital Signal Processing, 72: 192–207, 2018, doi: 10.1016/j.dsp.2017.10.006.
13. M. Sezgin, B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging, 13(1): 146–165, 2004, doi: 10.1117/1.1631315.
14. J. Jaybhay, R. Shastri, A study of speckle noise reduction filters, Signal and Image Processing: An International Journal, 6(3): 71–80, 2015, doi: 10.5121/sipij.2015.6306.
15. W. Niblack, An introduction to digital image processing, Prentice Hall Publishers, 1986.
16. J. Sauvola, M. Pietikäinen, Adaptive document image binarization, Pattern Recognition, 33(2): 225–236, 2000, doi: 10.1016/S0031-3203(99)00055-2.
17. D. Bradley, G. Roth, Adaptive thresholding using integral image, Journal of Graphics Tools, 12(2): 13–21, 2007, doi: 10.1080/2151237X.2007.10129236.
18. J.-S. Lee, Digital image enhancement and noise filtering by use of local statistics, IEEE Transactions on Pattern Analysis And Machine Intelligence, 2(2): 165–168, 1980, doi: 10.1109/TPAMI.1980.4766994.
19. J.-S. Lee, Speckle analysis and smoothing of synthetic aperture radar images, Computer Graphics and Image Processing, 17(1): 24–32, 1981, doi: 10.1016/S0146-664X(81)80005-6.
20. J.-S. Lee, Refined filtering of image noise using local statistics, Computer Graphics and Image Processing, 15(4): 380–389, 1981, doi: 10.1016/S0146-664X(81)80018-4.
21. J. Senthilnath, H.V. Shenoy, R. Rajendra, S.N. Omkar, V. Mani, P.G. Diwakar, Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction, Journal of Earth System Science, 122: 559–572, 2013, doi: 10.1007/s12040-013-0305-z.
22. V.P. Kharchenko, N.S. Kuzmenko, I.V. Ostroumov, An investigation of synthetic aperture radar speckle filtering and image segmentation considering wavelet decomposition, 2019 European Microwave Conference in Central Europe (EuMCE), pp. 398–401, May 2019.
23. X. Li, D.C. Liu, Ultrasound speckle reduction based on image segmentation and diffused region growing, [in:] 11th Joint International Conference on Information Sciences, pp. 338–344, 2008, doi: 10.2991/jcis.2008.58.
24. A.K. Boyat, B.K. Joshi, A review paper: Noise models in digital image processing, Signal and Image Processing: An International Journal, 6(2): 63–75, 2015, doi: 10.5121/sipij.2015.6206.
25. J.-M. Park, W.J. Song, W. Pearlman, Speckle filtering of SAR images based on adaptive windowing, IEEE Proceedings – Vision, Image and Signal Processing, 146(4): 191–197, 1999, doi: 10.1049/ip-vis:19990550.
26. Zhou Wang, A.C. Bovik, H.R. Sheikh, E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE transactions on image processing, 13(4): 600–612, 2004, doi: 10.1109/TIP.2003.819861.