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Journal of Healthcare Engineering
Volume 2017, Article ID 1848314, 10 pages
https://doi.org/10.1155/2017/1848314
Research Article

Quantitative Analysis of Intracellular Motility Based on Optical Flow Model

1College of Electronics and Information Engineering, Hebei University, Baoding 071002, China
2School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Correspondence should be addressed to Zhiwen Liu; nc.ude.tib@uilwz and Peiguang Wang; nc.ude.ubh@gnawgp

Received 17 February 2017; Accepted 21 May 2017; Published 30 July 2017

Academic Editor: Md. A. R. Ahad

Copyright © 2017 Yali Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Analysis of cell mobility is a key issue for abnormality identification and classification in cell biology research. However, since cell deformation induced by various biological processes is random and cell protrusion is irregular, it is difficult to measure cell morphology and motility in microscopic images. To address this dilemma, we propose an improved variation optical flow model for quantitative analysis of intracellular motility, which not only extracts intracellular motion fields effectively but also deals with optical flow computation problem at the border by taking advantages of the formulation based on and norm, respectively. In the energy functional of our proposed optical flow model, the data term is in the form of norm; the smoothness of the data changes with regional features through an adaptive parameter, using norm near the edge of the cell and norm away from the edge. We further extract histograms of oriented optical flow (HOOF) after optical flow field of intracellular motion is computed. Then distances of different HOOFs are calculated as the intracellular motion features to grade the intracellular motion. Experimental results show that the features extracted from HOOFs provide new insights into the relationship between the cell motility and the special pathological conditions.