Registering sequences of in vivo microscopy images for cell tracking using dynamic programming and minimum spanning trees

摘要

Registration of in vivo microscopy image sequences is important for tracking of cells. Registering a long sequence of in vivo microscopy images is particularly challenging for several reasons, which include motion artifacts created by the cardiac cycle and breathing movements of the living subject, occasional defocussing, illumination change, and noise in image acquisition. To accommodate these variations, we sample time points redundantly during microscopic image acquisition. Second, we use dynamic programming to select image frames with tolerable motion and eliminate those with large motion. Third, we employ a novel method based on the minimum spanning tree algorithm to register the selected image frames. Testing on actual in vivo image sequences reveals that our approach excels over three existing registration methods in terms of structural image similarity of the registered images.