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A gradient method used to identify object boundary in EIT image

机译:用于识别EIT图像中对象边界的梯度方法

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With the development of electronic technology and image reconstruction algorithm, the quality of EIT image is improved significantly. EIT technology has the potential to be of great value in medical and industrial applications. However, there is very little research about the estimation of anomaly's boundary from EIT image. The gradient method is proposed in this paper to identify object boundary from EIT image reconstructed by sensitivity conjugate gradient (SCG) algorithm. The performance of gradient method in terms of position error and size error is compared with traditional threshold method at two threshold levels through computer simulation and phantom experiments. The results show that the result of gradient method can cover the anomaly region in investigated situations and rely on very little priori-knowledge, while threshold method can estimate more accurately if the threshold value is set properly based on priori-knowledge. Improper threshold definition may lead to significant error in boundary identification. The pilot study to estimate the location and size of the anomaly from EIT image further completes EIT research and promotes its practical application.
机译:随着电子技术和图像重建算法的发展,EIT图像的质量显着提高。 EIT技术有可能在医疗和工业应用方面具有很大的价值。然而,关于异常从EIT图像估计的估计几乎没有研究。本文提出了梯度方法,以识别由灵敏度共轭梯度(SCG)算法重建的EIT图像的对象边界。将梯度法在定位误差和尺寸误差方面的性能与通过计算机仿真和幻像实验以两个阈值水平的传统阈值方法进行比较。结果表明,梯度方法的结果可以覆盖调查情况下的异常区域,并依赖于非常少的先验知识,而阈值方法可以更准确地估计,如果基于先验知识正确地设置阈值。不正确的阈值定义可能导致边界识别中的显着误差。试验研究估算了从EIT图像估计异常的位置和大小进一步完成了EIT研究并促进了其实际应用。

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