Automated Cell Tracking Using Adaptive Multi-stage Kalman Filter In Time-laps Images

Segmenting living cells and tracking their movement in microscopy images are significant in biological studies and have played a crucial role in disease diagnosis, targeted therapy, drug delivery, and many other medical applications. Due to a large amount of time-lapse image data, automated image analysis can be a proper alternative to manual analysis, which is unreasonably time-consuming. However, Low-resolution microscopic images, unpredictable cell behavior, and multiple cell divisions make automated cell tracking challenging. In this paper, we propose a novel multi-object tracking approach guided by a two-stage adaptive Kalman forecast. Cell segmentation is performed using an edge detector combined with various morphological operations. The tracking section includes two general stages. At first, a Kalman filter with a constant speed is used to estimate the position of each cell in consecutive frames. The primary Kalman filter was able to detect a significant percentage of cells, but the high rate of cell division and migration of cells in or out of the field of view has caused errors in the final result. In the next stage, a secondary Kalman filter with modified parameters extracted from the results of initial tracking is proposed to estimate the position of cells in each frame, decrease errors, and improve the tracking results. Experimental results indicate that our method is 94.37% accurate in segmenting cells. The validity of the whole method has been conducted by comparing the results of the proposed method with manual tracking results, which demonstrates its efficiency.