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Objective Cell morphological change is related to the cellular states and reflects the dynamic processes.So the quantitative analysis of cell morphology is critical for understanding the mechanism of physiological and pathological processes.This paper focuses on the feature extraction of cellular movements,aiming to achieve the quantitative analysis and classification of cellular morphological changes.Methods Motion history image is introduced to capture the deformation of cell boundary and local binary pattern describes the intracellular motions.Moreover,a series of temporal windows are adopted to encode the features into a multi-temporal feature vector and combined with the support vector machine to classify the cells with variance deformations.Experiments are provided to assort the lymphocytes into four groups according to their morphological states,taken from the blood samples of mice.Results The classification accuracy achieves 75% and indicates the effective of the proposed framework.Conclusions The analysis of cellular morphological changes in images leads to a comprehensive characterization of morphological motion and is more suitable for heterogeneous motion representation.The proposed method is discriminative for variant morphological changes,which can be used for the detection of abnormal cell morphological changes and assist in early diagnosis.