Granular filter in medical image noise suppression and edge preservation

• Autorzy: Wieclawek Wojciech , Pietka Ewa

Identyfikatory

DOI

10.1016/j.bbe.2018.09.006

• Języki publikacji: EN

Abstrakty

EN

An alternative non-linear filtering technique for medical image denoising while preserving edge is introduced. Two different variants of the approach i.e. crisp and fuzzy are developed. The solution is demonstrated based on US breast images as well as CT studies and gave promising results in comparison with commonly known and popular filtering techniques (i.e. spatial averaging and median, bilateral filter, anisotropic diffusion). Many different measures were used to evaluate the method. There are pixel-to-pixel error measures, structural information factors and edge preservation measures. The benefits are noticeable in all three categories.

Słowa kluczowe

EN

granular computing; filtering technique; ultrasound image; breast; computed tomography

PL

filtrowanie danych; obraz ultrasonograficzny; gruczoł piersiowy; tomografia komputerowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 1--16
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Wieclawek Wojciech

• Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland

autor

Pietka Ewa

• Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland

Bibliografia

• [1] Kaur P, Singh G, Kaur P. A review of denoising medical images using machine learning approaches. Curr Med Imaging Rev 2017;13:1–11. http://dx.doi.org/10.2174/1573405613666170428154156.
• [2] Irum I, Shahid MA, Sharif M, Raza M. A review of image denoising methods. Engi Sci Technol 2015;8(5):41–8.
• [3] Mredhula L, Dorairangasamy MA. Article: an extensive review of significant researches on medical image denoising techniques. Int J Comput Appl 2013;64(14):1–12.
• [4] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12(7):629–39. http://dx.doi.org/10.1109/34.56205.
• [5] Weickert J. Anisotropic Diffusion in Image Processing; 1996.
• [6] Gilboa G, Sochen N, Zeevi YY. Image enhancement and denoising by complex diffusion processes. IEEE Trans Pattern Anal Mach Intell 2004;26(8):1020–36. http://dx.doi.org/10.1109/TPAMI.2004.47.
• [7] Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271). 1998. pp. 839–46. http://dx.doi.org/10.1109/ICCV.1998.710815.
• [8] Durand F, Dorsey J. Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans Graph 2002;21 (3):257–66. http://dx.doi.org/10.1145/566654.566574.
• [9] Banterle F, Corsini M, Cignoni P, Scopigno R. A low-memory, straightforward and fast bilateral filter through subsampling in spatial domain. Comput Graph Forum 2012. http://dx.doi.org/10.1111/j.1467-8659.2011.02078.x.
• [10] Li H, Wu J, Miao A, Yu P, Chen J, Zhang Y. Rayleigh- maximum-likelihood bilateral filter for ultrasound image enhancement. BioMed Eng Online 2017;16(1):46. http://dx.doi.org/10.1186/s12938-017-0336-9.
• [11] Olfa M, Nawres K. Ultrasound image denoising using a combination of bilateral filtering and stationary wavelet transform. International Image Processing, Applications and Systems Conference. 2014. pp. 1–5. http://dx.doi.org/10.1109/IPAS.2014.7043258.
• [12] Raj VNP, Venkateswarlu T. Ultrasound medical image denoising using hybrid bilateral filtering. Int J Comput Appl 2012;56(14):44–51.
• [13] Loganayagi T. A multiresolution bilateral filter for speckle reduction in ultrasound kidney images. Asian J Res Soc Sci Hum 2016;6:119–31. http://dx.doi.org/10.5958/2249-7315.2016.00414.7.
• [14] Pedrycz W. Granular computing: analysis and design of intelligent systems, industrial electronics. Taylor & Francis; 2013.
• [15] Paris S, Kornprobst P, Tumblin J, Durand F. A gentle introduction to bilateral filtering and its applications. ACM SIGGRAPH 2007 Courses, SIGGRAPH'07; 2007. http://dx.doi.org/10.1145/1281500.1281602.
• [16] Paris S, Kornprobst P, Tumblin J, Durand F. Bilateral filtering: theory and applications. Found Trends Comput Graph Vis 2009;4(1):1–73. http://dx.doi.org/10.1561/0600000020.
• [17] Wieclawek W. Information granules in image histogram analysis. Comput Med Imaging Graph 2018;65:129–41. http://dx.doi.org/10.1016/j.compmedimag.2017.05.003. Advances in Biomedical Image Processing.
• [18] Bargiela A, Pedrycz W. Granular computing: an introduction. The Springer international series in engineering and computer science, Springer US; 2003.
• [19] Bargiela A, Pedrycz W. The roots of granular computing. Proceedings of 2006 IEEE International Conference on Granular Computing; 2006. p. 806–9.
• [20] Pedrycz W, Homenda W. Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 2013;13(10):4209–18. http://dx.doi.org/10.1016/j.asoc.2013.06.017.
• [21] Pedrycz W. The principle of justifiable granularity and an optimization of information granularity allocation as fundamentals of granular computing. J Inf Process Syst JIPS 2011;7(3):397–412. http://dx.doi.org/10.3745/JIPS.2011.7.3.397.
• [22] Soille P. Morphological image analysis: principles and applications. 2nd ed. Secaucus, NJ, USA: Springer-Verlag New York, Inc.; 2003.
• [23] Sattar F, Floreby L, Salomonsson G, Lövström B. Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process 1997;6(6):888–95. http://dx.doi.org/10.1109/83.585239.
• [24] Michel-González E, Cho MH, Lee SY. Geometric nonlinear diffusion filter and its application to X-ray imaging. BioMed Eng Online 2011;10(1):47. http://dx.doi.org/10.1186/1475-925X-10-47.
• [25] Jaya VL, Gopikakumari R. IEM: a new image enhancement metric for contrast and sharpness measurements. Int J Comput Appl 2013;79(9):1–9.
• [26] Peli E. Contrast in complex images. J Opt Soc Am A 1990;7(10):2032–40. http://dx.doi.org/10.1364/JOSAA.7.002032.
• [27] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13(4):600–12. http://dx.doi.org/10.1109/TIP.2003.819861
• [28] Wang Z, Bovik AC. A universal image quality index. IEEE Signal Process Lett 2002;9(3):81–4. http://dx.doi.org/10.1109/97.995823.
• [29] Tsiotsios C, Petrou M. On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recognit 2013;46(5):1369–81. http://dx.doi.org/10.1016/j.patcog.2012.11.012.
• [30] Mortensen EN, Barrett WA. Interactive segmentation with intelligent scissors. Graph Models Image Process 1998;60(5):349–84. http://dx.doi.org/10.1006/gmip.1998.0480.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).