Solution of electrical impedance tomography（ EIT） is a nonlinear inverse problem and its ill-posed problem is one of the reasons of low resolution of reconstructed images. In order to improve the ill-posedness and improve imaging quality

prior information and regularization term are introduced. The regularization matrix is reconstructed using the prior information

including the conductivity of organs and tissues and distribution structure of the human thorax. The regularization term is introduced into the state space expression of extended Kalman filtering（ EKF）

and the images of lung cancer issue are reconstructed. The simulation results indicate that the proposed method improves the imaging quality of the cancer tissue and reduce the relative error of the reconstructed images.