Image reconstruction via statistical classification for magnetic induction tomography

Magnetic induction tomography (MIT) is a non-invasive technology for visualization of the conductivity distribution inside inhomogeneous media. So far, the resolution of MIT has not been high enough for practical applications in biomedical imaging yet. In this research, we investigate the image reconstruction problem using statistical classification method to enhance the resolution of MIT. First, Tikhonov regularization or iteration Newton-Raphson algorithm is used to recover the initial conductivities of media understudy. Then, by setting a threshold on the basis of Otsu, the recovered conductivities are classified into some groups, whose labels can be obtained by some prior knowledge. Finally, according to the classification results, the conductivity distribution is spatially visualized. Simulation experiments are conducted, and the applicability and effectiveness of the proposed method are shown by compared with some other well developed methods.