A Novel Texture Extraction Method for Digital Radiography

Abstract:

As known, there always exist severely degradation problems in digital radiography. How we can extract necessary textures from degraded radiographic images is the post-processing key. Local binary pattern (LBP) is a well-known method, which is widely used in fast image texture extraction. However, for noisy images, LBP can’t work well due to its sensitivity to details. On the other hand, as one of the important shock filters developed in recent years, complex shock filter possesses excellent capabilities in textural image processing. Here, by combining complex shock filter with LBP, a novel fast and efficient method, C-LBP is presented for texture extraction of degraded radiographic images. Experimental results show that comparing with traditional LBP, C-LBP not only distinguishes between noise and details in radiographic images, but also extracts image textures efficiently and rapidly, which plays an important role in developing nondestructive detection technique by low-dose ray radiography.

• Periodical: Applied Mechanics and Materials (Volumes 719-720)
• Pages: 1148-1154
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.719-720.1148
• Online since: January 2015
• Authors: Shuang Qiao, Jia Ning Sun, Jian Li, Ji Peng Huang*
• Keywords: Complex Shock Filter, Digital Radiography, Local Binary Pattern, Texture Extraction

References

[1] Li J, Li LT, Cong P, Song Q, Wu ZF. Rotating polar-coordinate ART applied in industrial CT image reconstruction. NDT&E Int 2007; 40: 333–336.

DOI: 10.1016/j.ndteint.2006.11.005

[2] Yang M, Gao HD, Li XD, Meng FY, Wei DB. A new method to determine the center of rotation shift in 2D-CT scanning system using image cross correlation. NDT&E Int 2012; 46: 48–54.

DOI: 10.1016/j.ndteint.2011.09.001

[3] Kersting T, Schonartz N, Oesterlein L, Liessern A. High end inspection by filmless radiography on LSAW large diameter pipes. NDT&E Int 2010; 43: 206–209.

DOI: 10.1016/j.ndteint.2009.11.004

[4] Casalta S, Daquino GG, Metten L, Oudaert J, Van de Sande A. Digital image analysis of X-ray and neutron radiography for the inspection and the monitoring of nuclear materials. NDT&E Int 2003; 36: 349–355.

DOI: 10.1016/s0963-8695(03)00008-2

[5] Ojala T, Pietikainen M, Harwood. D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 1996; 29: 51–59.

DOI: 10.1016/0031-3203(95)00067-4

[6] Pietikainen M, Ojala T, Xu Z. Rotation-invariant texture classification using feature distributions. Pattern Recognition 2000; 33: 43–52.

DOI: 10.1016/s0031-3203(99)00032-1

[7] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intelligence, 2002; 24: 971–987.

DOI: 10.1109/tpami.2002.1017623

[8] Ahonen T, Hadid A, Pietikainen M. Face recognition with local binary patterns. Computer Vision-Eccv; 2004. LNCS 3021: 469-481.

DOI: 10.1007/978-3-540-24670-1_36

[9] Zhao SQ, Gao YS, Zhang BC. SOBEL-LBP. In: Proceedings of IEEE international conference on Image Processing (ICIP); 2008. p.2144–2147.

[10] Tan XY, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Processing 2010; 19: 1635–1650.

DOI: 10.1109/tip.2010.2042645

[11] Wang Q, Liang JM, Hu ZJ, Hu HH, Zhao H, Hu HQ, et al. Spatial texture based automatic classification of dayside aurora in all-sky image. J Atmos Sol Terr Phys 2010; 72: 498–508.

DOI: 10.1016/j.jastp.2010.01.011

[12] Peng SH, Kim DH, Lee SL, Lim MK. Texture feature extraction based on uniformity estimation method for local brightness and structure in chest CT images. Comput Biol Med 2010; 40: 931–942.

DOI: 10.1016/j.compbiomed.2010.10.005

[13] Azab MM, Shedeed HA, Hussein AS. A new technique for background modeling and subtraction for motion detection in real-time videos. In: Proceedings of IEEE international conference on Image Processing (ICIP); 2010. p.3453–3456.

DOI: 10.1109/icip.2010.5653748

[14] Sun H, Wang C, Wang BL, El-Sheimy N. Pyramid binary pattern features for real-time pedestrian detection from infrared videos. Neurocomputing 2011; 74: 797–804.

DOI: 10.1016/j.neucom.2010.10.009

[15] Wang KB, Yu BZ, Xi W. Radar image segmentation using active contour. In: Proceedings of 2007 1st Asian and Pacific conference on synthetic aperture radar pages; 2007. p.446–450.

DOI: 10.1109/apsar.2007.4418732

[16] Subrahmanyam M, Maheshwari RP, Balasubramanian R. Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking. Signal Processing 2012; 92: 1467–1479.

DOI: 10.1016/j.sigpro.2011.12.005

[17] Li Y, Luo S, Zou Q. Learning to detect boundaries in natural image using texture cues and EM. In: Proceedings of 2008 fourth international conference on natural computation (ICNC); 2008. p.167–171.

DOI: 10.1109/icnc.2008.233

[18] Savelonas MA, Iakovidis DK, Maroulis D. LBP-guided active contours. Pattern Recognition Letter 2008; 29: 1404–1415.

DOI: 10.1016/j.patrec.2008.02.013

[19] Guy Gilboa, Nir A. Sochen, and Yehoshua Y. Zeevi. Regularized Shock Filters and Complex Diffusion[J] Computer Vision-ECCV 2002 Lecture Notes in Computer Science Volume 2350, 2002: 399-413.

DOI: 10.1007/3-540-47969-4_27

[20] Yang MQ, Peng YH, Liu YX. The algorithm and application of finite line integral transform. In: Proceedings of IEEE 2005 international symposium on microwave, antenna, propagation and EMC technologies for wireless communications proceedings; 2005. Vol 1 and 2. p.411.

DOI: 10.1109/mape.2005.1617936