基于旋转电极的电容层析成像技术图像融合算法

Image Fusion Algorithm-based Rotating Electrodes for Electrical Capacitance Tomography

针对固定电极的电容层析成像技术独立测量值较少,且由于电极位置的影响而导致重建图像失真等问题,提出了一种基于16旋转电极的电容层析成像技术系统模型及对应的图像融合方法。模型对16电极的电容层析成像技术模型进行4次旋转,得出的数据分别采用线性反投影算法和修正共轭梯度法算法进行图像重建,再将重建的5张图像进行小波变换,变换得到的低频和高频成分分别采用加权平均和主分量分析的融合准则进行图像融合。实验结果表明:提出的电容层析成像技术旋转模型通过增加测量电容数,结合图像融合方法可明显提高重构图像质量,降低成像误差。

Since electrical capacitance tomography system with fixed electrodes has the drawbacks of limited measurement data and image distortion caused by the electrodes position, a novel electrical capacitance tomography model is proposed based on 16 rotating electrodes, together with a corresponding image fusion method. This model rotates a 16-electrode electrical capacitance tomography sensor four times, and the resulted measurements are used to reconstruct images by using linear back-projection and modified conjugate gradient （MCG） respectively, and thereafter wavelet transform is performed on the 5 reconstructed images, and the resulted low frequency and high frequency components are fused with the weighted average method and the principal component analysis （PCA） respectively. Experimental results show that by increasing the number of measured capacitance values and processing data with image fusion method, the proposed rotating ECT model can obviously improve the quality of reconstructed image and reduce the imaging error.