Quantitative analysis of live lymphocytes morphology and intracellular motion in microscopic images. (April 2015)

• Record Type: Journal Article
• Authors: Huang, Yali; Liu, Zhiwen; Shi, Yonggang
• Is Part Of: Biomedical signal processing and control. Volume 18(2015)
• Page Start: 195
• Page End: 203
• Publication Date: 2015-04
• Subjects: Lymphocyte shape analysis -- Lymphocyte deformation analysis -- Intracellular motion analysis -- Optical flow; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.01.003
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Intracellular motion of lymphocyte by color coding. Highlights: The dynamic behavior of live lymphocytes is quantitatively analyzed. A novel approach for exploiting intracellular motion information is proposed. An optimal feature vector is chosen by formal statistical testing. Effectiveness of the feature vector is experimentally demonstrated. Salient clinical potentials of the proposed scheme are envisioned. Abstract: Cellular morphology and motility analysis is a key issue for abnormality identification and classification in the research of relevant biological processes. Quantitative measures are beneficial to clinicians in making their final diagnosis. This article presents a new method for measurement of live lymphocyte morphology and intracellular motion (motility) in microscopic images acquired from peripheral blood of mice post skin transplantation. Our new method explores shape, deformation and intracellular motion features of live lymphocytes. Especially, a novel way of exploiting intracellular motion information based on optical flow method is proposed. On the basis of statistical tests, optimal morphological and motility features are chosen to form a feature vector that characterizes the dynamic behavior of the lymphocytes (including shape, deformation and intercellular motion). In order to evaluate the proposed scheme, the above feature vector is used as input to a probabilistic neural network (PNN) which then classifies the dynamic behavior of lymphocytes in a set of cell image sequences into normal and abnormal categories. Comparative experiments are conducted to validate the proposed scheme, and the results revealed that joint features of shape, deformation and intracellular motion achieve the best performance in expressing the dynamic behavior of lymphocytes, compared with Fourier descriptor and Zernike moment methods.