Fast and robust automated segmentation of EIT lung images using an anatomically constrained Kalman filter

In this paper we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using Electrical Impedance Tomography (EIT). EIT is an emerging promising non-invasive imaging modality, which produces real-time poor-spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a non-linear ill-posed inverse problem, therefore the problem is usually linearized which produces impedance-change images rather than static impedance, and the images are highly blurry and fuzzy along the object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed by augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle.