Automatic Tracking of Escherichia Coli Bacteria

Abstract

Abstract. In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy videos. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in sequence of frames. Then a novel matching gain measure is introduced to cope with the challenges such as dramatic changes of cells' appearance and serious overlapping and occlusion. For multiple cell tracking, an optimal matching strategy is proposed to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually-determined trajectories, as well as those obtained from existing tracking methods. The stability of the algorithm with different parameter values is also analyzed and discussed.

Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX

“…1B(c)) where dissected curves are linked together respecting both local cue and temporal information from previous frames, in order to enforce a "consistent partition" of the network over time. This topological complexity is usually not present in the task of tracking point objects such as cells [Chen et al, 2015, Xie et al, 2008, tips of microtubules [Altinok et al, 2006], or other point features in natural images [Shafique and Shah, 2005].…”

“…Formulating multi-frame object tracking as finding the minimum or maximum path cover in a k-partite graph has been successful for tracking cells [Xie et al, 2008], tips of microtubules [Altinok et al, 2006], and point features in natural images [Shafique and Shah, 2005]. Beside low computation complexity, the major benefit of this formulation is that it allows for false negative/positive classification of detection results (that aids when detection is influenced by image noise), and appearing/disappearing/reappearing of tracked objects.…”

İçme Sularında Bulunan E. Coli Bakterilerinin Görüntü İşleme Yöntemleriyle Tespiti

“…coli bakterilerinin tespit edilmesi için çok çeşitli yöntemler geliştirilmiştir. Bu yöntemlerin arasında bakterilerin ışıma yapan kimyasallar ile etiketlenerek veya mangan katkılı çinko gibi çeşitli mineraller kullanıldıktan sonra mikroskop altında incelendiği çalışmalar mevcuttur [12][13][14][15][16]. Ayrıca E. coli bakteri tespitinin mikroskop kullanılmadan büyütmesiz olarak yapılması da mümkündür.…”

Tracking of cell populations to understand their spatio-temporal behavior in response to physical stimuli

“…Notable is also the cell tracking system by Smith et al [21], which employs a Markov chain Monte Carlo batch processing approach to track a hundred migrating neurons in two-photon excitation microscopy. Xie et al [24] recently solved the problem of tracking Escherichia coli bacteria in microscopy video using a greedy assignment algorithm [20]. They introduced a matching criteria that compares intensity histograms of newly detected and previously tracked cells to address the issue that the bacteria were imaged with very-low contrast boundaries.…”