TY - JOUR
T1 - Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering
AU - Smal, Ihor
AU - Meijering, Erik
AU - Draegestein, K
AU - Galjart, Niels
AU - Grigoriev, I
AU - Akhmanova, Anna
AU - van Royen, Martin
AU - Houtsmuller, Adriaan
AU - Niessen, Wiro
PY - 2008
Y1 - 2008
N2 - Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions. (C) 2008 Elsevier B.V. All rights reserved.
AB - Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions. (C) 2008 Elsevier B.V. All rights reserved.
U2 - 10.1016/j.media.2008.03.004
DO - 10.1016/j.media.2008.03.004
M3 - Article
SN - 1361-8415
VL - 12
SP - 764
EP - 777
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 6
ER -