Tracking On Active Cells In Image Sequences Based On EKF-PF

The cells motion tracking study has been an important part of Biologi, Biomed andcytology. Although active cells are a very small proportion of cells in image sequences, it hasa very important role. Active cells’ accurate prediction and tracking in image sequences is stillan unsolved problem. Establishing cell motion model and finding an accurate and effectivecell tracking algorithm are the key of subject study in the paper.The paper applies Extended Kalman Particle Filter (EKF-PF) to active cells predictionand tracking in image sequences. The algorithm mainly applies to the nonlinear andnonGaussian system, and cell motion in image sequences is mostly nonlinear andnonGaussian, especially active cells. So the algorithm which applies to predict and track ismore conformed to active cells motion characteristics. Usually cell tracking mostly adoptsconstant velocity or constant acceleration motion model, but active cells motion is not simpleconstant velocity or constant acceleration motion. So the paper present non-zero mean valuetime-correlation model of the maneuvering acceleration to simulate active cells motion model.The motion which the model describes is between constant velocity motion and constantacceleration motion, which is more conformed to active cells motion characteristics. Thegeneral motion model only uses the target position, velocity and acceleration information toestablish, which did not consider the target motion angle information. In order to predict andtrack active cells more accurately and effectively, cell motion angle information is added toimprove the motion model when it is established. We usually use six criteria, which are cellmotion distance change, cell motion velocity change, cell diameter change, cell area change,cell azimuth change and cell centrifugal rate change, to establish the cells’ cost function. Inorder to reduce the calculated amount, we only use three criteria, which are cells motiondistance change, cells diameter change and cells azimuth change, to improve the cells’ costfunction.Through the performance of the particle filter fundamental algorithms and several kindsof the modified algorithms for comparative analysis, we finally choose the EKF-PF algorithmto predict and track active cells, mainly because the algorithm takes full advantage of the newobservations, the important probability distribution is more close to the posterior probabilitydistribution, which can more accurately and effectively predict and track active cells in image sequences. At the same time the algorithm calculated amount is not too large, which can betterto meet the real time of prediction and tracking.There are fourteen active cells in three image sequences which have been predicted andtracked in the paper simulation experiment. The experimental results show that the errors ofactive cells estimated values and observation values in three image sequences are less than2.5pixels, so the EKF-PF algorithm may approach to the solution of accurately and effectivelyprediction and tracking of the active cells.