The characterization of the thrombus formation in time-lapse DIC microscopy is of increased interest for identifying genes which account for atherothrombosis and coronary artery diseases (CADs). In particular, we are interested in large-scale studies on zebrafish, which result in large amount of data, and require automatic processing. In this work, we present an automatized solution system for extraction of parameters quantifying the temporal development of a thrombus. Our system is based on the joint segmentation of the thrombus and the aortic vessel in time. This task is made difficult by the low contrast and the highly dynamic conditions observed in in-vivo DIC microscopic scenes. Our key idea is to perform this segmentation by distinguishing the different motion patterns in the image time series rather than by performing a standard image segmentation task in each image frame. Thus, we are able to compensate for the poor imaging conditions. We model the motion patterns by energies based on the idea of dynamic textures, and regularize the model by two prior energies on the shape of the aortic region and on the topological relationship between the thrombus and the aorta. We demonstrate the performance of the presented segmentation algorithm by qualitative and quantitative experiments on synthetic examples as well as on real in-vivo microscopic sequences.     «

The characterization of the thrombus formation in time-lapse DIC microscopy is of increased interest for identifying genes which account for atherothrombosis and coronary artery diseases (CADs). In particular, we are interested in large-scale studies on zebrafish, which result in large amount of data, and require automatic processing. In this work, we present an automatized solution system for extraction of parameters quantifying the temporal development of a thrombus. Our system is based on the...     »