Article

Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images: Application to Microtubule Growth Analysis

Dept. of Med. Inf., Erasmus MC-Univ. Med. Center, Rotterdam
IEEE Transactions on Medical Imaging (Impact Factor: 3.8). 07/2008; 27(6):789 - 804. DOI: 10.1109/TMI.2008.916964
Source: IEEE Xplore

ABSTRACT Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework. It better exploits spatiotemporal information and prior knowledge than common approaches, yielding more robust tracking also in cases of photobleaching and object interaction. The tracking method was evaluated using simulated but realistic image sequences, for which ground truth was available. The results of these experiments show that the method is more accurate and robust than popular tracking methods. In addition, validation experiments were conducted with real fluorescence microscopy image data acquired for microtubule growth analysis. These demonstrate that the method yields results that are in good agreement with manual tracking performed by expert cell biologists. Our findings suggest that the method may replace laborious manual procedures.

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Available from: W.J. Niessen, Aug 18, 2013
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    • "where each element of b takes the value of the background intensity I b . Note that a similar image likelihood is also used to compute the weights of samples in tracking approaches based on particle filters (e.g., [19], [21]). Once all weights have been evaluated with the image likelihood p(z|x), the weights β i , i = 1, . . . "
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    ABSTRACT: Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to re-calculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2D and 3D images as well as to real 2D and 3D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2D and 3D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.
    IEEE Transactions on Medical Imaging 09/2014; 34(2). DOI:10.1109/TMI.2014.2359541 · 3.80 Impact Factor
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    • "The positions and directions of motion of the objects are randomly chosen within the image plane. The speed (i.e., the displacement in pixels per frame) is drawn uniformly at random over the interval [2] [7] for large objects and over [2] [4] for small objects. The SNR of the images of large objects is 2 (ca. "
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    ABSTRACT: Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the approximate sequential importance sampling/resampling (ASIR) algorithm, which aims at reducing the cost of traditional particle filters by approximating the likelihood with a mixture of uniform distributions over pre-defined cells or bins. The particles in each bin are represented by a dummy particle at the center of mass of the original particle distribution and with a state vector that is the average of the states of all particles in the same bin. The likelihood is only evaluated for the dummy particles, and the resulting weight is identically assigned to all particles in the bin. We derive upper bounds on the approximation error of the so-obtained piecewise constant function representation, and analyze how bin size affects tracking accuracy and runtime. Further, we show numerically that the ASIR approximation error converges to that of sequential importance sampling/resampling (SIR) as the bin size is decreased. We present a set of numerical experiments from the field of biological image processing and tracking that demonstrate ASIR's capabilities. Overall, we consider ASIR a promising candidate for simple, fast particle filtering in generic applications.
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    • "The dynamics model assumes nearly constant velocity, and the appearance model approximates each object by Gaussian intensity profile in the final microscopy image. These are standard models that adequately describe biological fluorescence microscopy [13], [14]. The state vector in this case is x = (ˆ x, ˆ y, v x , v y , I 0 ) T , wherê x andˆy are the estimated x-and y-positions of the object, (v x , v y ) its velocity vector, and I 0 its estimated fluorescence intensity. "
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    ABSTRACT: Distributed resampling algorithm with proportional allocation (RNA) draws serious attention to parallel systems and how they can be used in particle tracking applications. We extend the original work by Boli\'c et al. by introducing the adaptive RNA (ARNA), which improves RNA by enabling (1) adjustable particle exchange ratio and (2) randomized ring topology. These features of ARNA boost the runtime performance of the fastest RNA (i.e., RNA with 10% particle exchange ratio) by 9%. In such parallel settings, it is important to have all processing elements (PE) tracking the object and thus keeping a high PE efficiency percentage $PE_{\textrm{eff}}$. ARNA shows 25-times $PE_{\textrm{eff}}$ improvement over the RNA methods in a network of 384 PEs. Moreover, the ARNA algorithm requires only few modifications in the original RNA code and thus ARNA is considered a better alternative to RNA.
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