Mitosis event recognition and detection based on evolution of feature in time domain

Mitosis detection and recognition in phase-contrast microscopy image sequences is a fundamental problem in many biomedical applications. Traditionally, researchers detect all mitotic cells from these image sequences with human eyes, which is tedious and time consuming. In recent years, many computer vision technologies were proposed to help humans to achieve the mitosis detection automatically. In this paper, we present an approach which utilized the evolution of feature in the time domain to represent the feature of mitosis. Firstly, the feature of each cell image is extracted by the different method (GIST, SIFT, CNN). Secondly, we construct the levels of motorists according to the steps of mitosis. The pooling method is utilized to handle the feature fusion in each dimension and in different time segments. Third, the pooling features were combined to one vector to represent the characters of this video. Finally, tradition machine learning method SVM is used to handle the mortises recognition problem. In order to demonstrate the performance of our approach, motorists event detection is made in some microscopy image sequences. In the experiment, some classic methods as comparison method are made in this paper. The corresponding experiments also demonstrate the superiority of our approach.