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X-ray imaging based on variable energy can expand the dynamic range of the imaging system and perfectly show the structure information of the detection objects, by acquiring and fusing the image sequences. However, the fusion method is ordinarily based on image quality optimization, and neglects the gray mapping accuracy of the actual high dynamic imaging. It cannot guarantee the physical matching between the image information and actual structure information. Therefore, in this paper we propose an X-ray image gray characterization algorithm of high dynamic fusion based on variable energy. First, take a standard wedge block as test object, and use the fusion image of low dynamic image sequences as input data. The output data are the actual high dynamic image. Then establish the X-ray imaging gray characterization model by neural network training. At the same time, because the attenuation coefficients of different heterogeneous materials are different, a modified model of physical characterization is established to achieve a correct characterization of real object. Finally, experiments by 12 bit and 16 bit imaging systems acquire the variable voltage image sequences using 12 bit detector. After image fusion, image mapping and gray level correction, the output image not only achieves the same effect of 16 bit detector, but also satisfies the gray relation. Also this method can effectively expand the dynamic range of the imaging system.
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Keywords:
- variable energy /
- high dynamic /
- gray characterize /
- neural network
[1] Bi B, Li Z, Kuan S, Haina J 2013 NDT E Int. 58 26
[2] Krämer P, Weckenmann A 2010 Measur. Sci. Technol. 21 1
[3] Chen P, Han Y, Pan J X 2013 Optik 124 3265
[4] Chen P, Han Y, Pan J X 2013 Spectrosc. Spectr. Anal. 33 1383 (in Chinese) [陈平, 韩焱, 潘晋孝 2013光谱学与光谱分析 33 1383]
[5] Liu B, Han Y, Pan J, Chen P 2014 J. X Ray Sci. Technol. 22 241
[6] Wei J T, Chen P, Pan J X 2013 Chin. J. Stereol. Image Anal. 18 103 (in Chinese) [魏交统, 陈平, 潘晋孝 2013 中国体视学与图像分析 18 103]
[7] Yang Y, Mou X Q, Luo T, Tang S J 2009 Acta Photon. Sin. 38 2435 (in Chinese) [杨莹, 牟轩沁, 罗涛, 汤少杰 2009 光子学报 38 2435]
[8] Fan J D, Jiang H D 2012 Acta Phys. Sin. 61 218702 (in Chinese) [范家东, 江怀东 2012 物理学报 61 218702]
[9] Liu L X, Du G H, Hu W, Xie H L, Xiao T Q 2007 Acta Phys. Sin. 56 4556 (in Chinese) [刘丽想, 杜国浩, 胡雯, 谢红兰, 肖体乔 2007 物理学报 56 4556]
[10] Wang J, Zhang H, Cheng X L 2013 Chin. Phys. B 22 085201
[11] Zhang J, Wang X W, Sun Y D 2011 Comput. Tomography Theory Appl. 20 235 (in Chinese) [张健, 王学武, 孙运达 2011 CT理论与应用研究 20 235]
[12] Lee W J, Kim D S, Kang S W, Yi W J 2012 34th Annual International Confer-Ence of the IEEE Engineering in Medicine and Biology Society California, USA, August 28-September 1, 2012 p1514
[13] Torbati N, Ayatollahi A, Kermani A 2014 Comput. Biol. Med. 44 76
[14] Funahashi K 1989 Neural Networks 2 183
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[1] Bi B, Li Z, Kuan S, Haina J 2013 NDT E Int. 58 26
[2] Krämer P, Weckenmann A 2010 Measur. Sci. Technol. 21 1
[3] Chen P, Han Y, Pan J X 2013 Optik 124 3265
[4] Chen P, Han Y, Pan J X 2013 Spectrosc. Spectr. Anal. 33 1383 (in Chinese) [陈平, 韩焱, 潘晋孝 2013光谱学与光谱分析 33 1383]
[5] Liu B, Han Y, Pan J, Chen P 2014 J. X Ray Sci. Technol. 22 241
[6] Wei J T, Chen P, Pan J X 2013 Chin. J. Stereol. Image Anal. 18 103 (in Chinese) [魏交统, 陈平, 潘晋孝 2013 中国体视学与图像分析 18 103]
[7] Yang Y, Mou X Q, Luo T, Tang S J 2009 Acta Photon. Sin. 38 2435 (in Chinese) [杨莹, 牟轩沁, 罗涛, 汤少杰 2009 光子学报 38 2435]
[8] Fan J D, Jiang H D 2012 Acta Phys. Sin. 61 218702 (in Chinese) [范家东, 江怀东 2012 物理学报 61 218702]
[9] Liu L X, Du G H, Hu W, Xie H L, Xiao T Q 2007 Acta Phys. Sin. 56 4556 (in Chinese) [刘丽想, 杜国浩, 胡雯, 谢红兰, 肖体乔 2007 物理学报 56 4556]
[10] Wang J, Zhang H, Cheng X L 2013 Chin. Phys. B 22 085201
[11] Zhang J, Wang X W, Sun Y D 2011 Comput. Tomography Theory Appl. 20 235 (in Chinese) [张健, 王学武, 孙运达 2011 CT理论与应用研究 20 235]
[12] Lee W J, Kim D S, Kang S W, Yi W J 2012 34th Annual International Confer-Ence of the IEEE Engineering in Medicine and Biology Society California, USA, August 28-September 1, 2012 p1514
[13] Torbati N, Ayatollahi A, Kermani A 2014 Comput. Biol. Med. 44 76
[14] Funahashi K 1989 Neural Networks 2 183
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