Separation of multi-energy X-ray imaging based on sparse processing

Bin FEI; Jing-yang SUN; Jun-ju ZHANG; Chun-yu YU

doi:10.3788/OPE.20172504.1106

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Separation of multi-energy X-ray imaging based on sparse processing

Information Sciences | 更新时间：2020-07-07

- Separation of multi-energy X-ray imaging based on sparse processing
- Optics and Precision Engineering Vol. 25, Issue 4, Pages: 1106-1111(2017)
- 作者机构：
  
  1.南京邮电大学光电工程学院, 江苏南京 210023
  2.南京理工大学电子工程与光电技术学院, 江苏南京 210094
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172504.1106
  CLC： TP391.41
- Received：04 November 2016，
  
  Accepted：12 December 2016，
  
  Published：25 April 2017
- 稿件说明：
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Bin FEI, Jing-yang SUN, Jun-ju ZHANG, et al. Separation of multi-energy X-ray imaging based on sparse processing[J]. Optics and precision engineering, 2017, 25(4): 1106-1111.
DOI：

Bin FEI, Jing-yang SUN, Jun-ju ZHANG, et al. Separation of multi-energy X-ray imaging based on sparse processing[J]. Optics and precision engineering, 2017, 25(4): 1106-1111. DOI： 10.3788/OPE.20172504.1106.

摘要

利用独立成分分析（Independent Component Analysis，ICA）并结合多能X射线图像的丰富信息可以将二维X射线图像中重叠目标分离成像，但是海量的图像数量，以及高像素数的要求均会使内存占有量和计算速度面临挑战，因此本研究将压缩感知（Compressed Sensing，CS）与ICA相结合进行分离成像，以提高计算速度和分离成像性能。研究过程中，首先根据被拍摄物体的物质组成确定拍摄多能X射线图像数量，并选取CS技术中K均值奇异值分解（K-means SingularValue Decomposition，K-SVD）稀疏基将多能X射线图像进行稀疏表示，然后利用ICA将此稀疏表示进行盲源分离得到独立源，最后采用正交匹配追踪算法（Orthogonal Matching Pursuit，OMP）将独立源进行重构实现分离成像。研究结果表明：采用ICA & CS技术比仅采用ICA进行目标分离成像的运行时间减少了46.14 s（23.3%）、内存占有率降低了21%、重构图像峰值信噪比（Peak Signal to Noise Ratio，PSNR）提高了2.665 dB、边缘梯度提高了0.001、信息熵提高了0.09。

Abstract

Independent Component Analysis (ICA) combined with abundant information of multi-energy X-ray images can achieve the imaging separation of overlapping targets in 2D X-ray images

but the increasing number of images and higher pixel requirements may serve as an obstacle for memory occupancy and calculating speed. In this paper

Compressed Sensing (CS) was combined with ICA to achieve the imaging separation and to improve the calculating speed

as well as the imaging separation performance. First

the number of the multi-energy X-ray images was determined based on composition of the captured object

and then sparse representation of multi-energy X-ray images was carried out by selecting K-means Singular Value Decomposition (K-SVD) in the CS technology; then

Blind Source Separation (BSS) was conducted in such sparse representation to obtain the independent source by using ICA; finally

Orthogonal Matching Pursuit (OMP) was used to reconstruct the independent source

thus achieving the imaging separation. The results show that compared with the algorithm merely based on ICA

ICA & CS could reduce the algorithm running time by 46.14 s (23.3%) and memory occupancy by 21%

and improve the Peak Signal to Noise Ratio (PSNR) of the reconstructed image by 2.665 dB

edge gradient by 0.001 and information entropy by 0.09.

关键词

Keywords

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