Optimized kalman filter based on second momentum and triple rectangular for cell tracking on sequential microscopic images

Cell dynamics and motion stages are very important issues in the biological cell investigation in this novel method, we propose a novel method based on Kalman filter and second momentum for tracking cells on Sequential Microscopic Images. In proposed manner at first, we select a cell and cut covering rectangle. in the next step, we predict rectangle center of the cell in Next frame based on a modeling of velocity-acceleration using Kalman filter. The rectangle with triple covering area of previous cell rectangle and predicting center by Kalman filter is considered as a searching area. So, if all objects in the search areas have second momentum error less than threshold, it is selected as a chosen cell and we continue this process till all cells are track. OKF (optimized Kalman filter) algorithm uses second momentum for detecting cells because; value of second momentum doesn´t change with rotating cells or changing size. OKF method removes cell after tracking in every frame. So, tracking has an acceptable and excellent precision for tracking in another cell. Also, the algorithm finds cells between frames by changing the reference frame and removing were tracked cells. Performance results of OKF show the proposed method is very admissible, effective method and the work shows great assistance for the cell Cytoplasm tracking. Finally, cells tracking with 98.17% accuracy were achieved by using OKF algorithm.