Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

Abstract

SummaryPhase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration fax: +358 8 537 6115; e-mail: lauri.eklund@oulu.fi of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells.

The interplay of matrix metalloproteinase-8, transforming growth factor-β1 and vascular endothelial growth factor-C cooperatively contributes to the aggressiveness of oral tongue squamous cell carcinoma

“…The analyses were performed with MATLAB (MathWorks, Natick, MA, USA). The application of the combination of the segmentation and tracking approaches for cell segmentation from phase-contrast images has been described in detail previously ( Kaakinen et al , 2014 ). Six samples were studied for each cell group.…”

Preliminary Investigation of the Robustness of Maximally Stable Extremal Regions (Mser) Model for the Automatic Registration of Overlapping Images

“…This is in tandem with the results obtained by (Mikolajczyk et al, 2005) which attests to the speed of MSER when compared to 5 other detectors. An improved computational time was also recorded by Nistér & Stewénius (2008) and (Kaakinen et al, 2013) and this is as a results of MSER's negligible need for pre-optimization procedures which dramatically reduces its computation time (Kaakinen et al, 2013).…”

“…An MSER is a stable connected component of some gray-level sets of the image and it is based on the idea of extracting regions which stay nearly the same through a wide range of thresholds. While MSER has been widely and successfully applied in different image processing applications (Mikolajczyk et al, 2005) (Fraundorfer & Bischof, 2005) some of which include tracking and 3D segmentation (Donoser & Bischof, 2006), retrieval or restoration of images (Nister & Stewenius, 2006), matching of wide baselines (Matas et al, 2004) and curvilinear structures (Lemaitre et al, 2011), object recognition (Obdrzalek & Matas, 2002), real-time visual surveillance (Salahat et al, 2015), Field Programmable Gate Array-FPGA (Kristensen & MacLean, 2007), cell detection and analysis (Kaakinen et al, 2013), etc, research efforts aimed at implementing it for mosaic generation or automatic image registration is relatively unknown. This paper presents some preliminary findings of the investigation of the robustness of MSER for the automatic registration of overlapping image pairs using images acquired from Trimble Ux-5 Unmanned Aerial Vehicles (UAV) and google earth online image data repository.…”

Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle

“…Image analysis of video recordings was performed by Kalman-based tracking, essentially as described for cell motility analysis in phase contrast microscopy [ 40 ]. Each extracted object segment was attached to a Kalman filter with a constant velocity motion model having 20 pixel motion noise.…”