Rao-Blackwellized Marginal Particle Filtering for Multiple Object Tracking in Molecular Bioimaging

Smal, Ihor; Draegestein, Katharina; Galjart, Niels; Niessen, Wiro; Meijering, Erik

doi:10.1007/978-3-540-73273-0_10

Cited by 20 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the problems faced by feature-based tracking algorithms, motion prediction techniques such as those based on Kalman filtering and Particle filtering were developed [6][7]. These approaches proved to be robust only in situations when the cellular motion can be approximated by statistical models.…”

Section: Previous Methodsmentioning

confidence: 99%

Cellular Tracking in Time-Lapse Phase Contrast Images

Thirusittampalam

Hossain

Ghita

et al. 2009

2009 13th International Machine Vision and Image Processing Conference

View full text Add to dashboard Cite

show abstract

Section: Previous Methodsmentioning

confidence: 99%

Cellular Tracking in Time-Lapse Phase Contrast Images

Thirusittampalam

Hossain

Ghita

et al. 2009

2009 13th International Machine Vision and Image Processing Conference

View full text Add to dashboard Cite

show abstract

“…To do this we add edges to the graph that allow connections up to M sections away: (7) In this paper, we use M = 2, thereby allowing connections between sections separated by at most a single intermediate section. This choice gives Dijkstra's algorithm a choice in calculating the best path in the case where an immediately adjacent section does not have the best match.…”

Section: Extension To Robust Optimal-path-findingmentioning

confidence: 99%

“…Tracking methods that use active contour based tracking models [4,5] work well for identifying features that contain small amounts of variation between sections and individual structures with little variability. Probabilistic frameworks [6,7] use tracking in the more traditional sense to identify features in time lapse light microscopy. The problems we address in this paper is also different from cell tracking in light microscopy Properties of light microscopy images are very different than TEM images; therefore these methods do not apply to the problem discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

An optimal-path approach for neural circuit reconstruction

Jurrus

Whitaker

Jones

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

View full text Add to dashboard Cite

Neurobiologists are collecting large amounts of electron microscopy image data to gain a better understanding of neuron organization in the central nervous system. Image analysis plays an important role in extracting the connectivity present in these images; however, due to the large size of these datasets, manual analysis is essentially impractical. Automated analysis, however, is challenging because of the difficulty in reliably segmenting individual neurons in 3D. In this paper, we describe an automatic method for finding neurons in sequences of 2D sections. The proposed method formulates the problem of finding paths through sets of sections as an optimal path computation, which applies a cost function to the identification of a cell from one section to the next and solves this optimization problem using Dijkstra's algorithm. This basic formulation allows us to account for variability or inconsistencies between sections and to prioritize cells based on the evidence of their connectivity.

show abstract

“…The most commonly-used tracking approach is based on motion correspondence [2,3]: the particles are detected independently in each frame in a first step, and then the trajectories are computed by connecting the detected objects over time. Sophisticated particle filtering techniques [4,5], graph-theory based methods [6] or minimal paths methods [7] have been also developed to improve temporal matching. In this paper, we address the problem of spatial detection of fluorescence irregularities in 2D images of temporal sequences obtained in time-lapse fluorescence microscopy.…”

Section: Introductionmentioning

confidence: 99%

Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

Pécot

Kervrann

Bardin

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

View full text Add to dashboard Cite

Abstract. The study of protein dynamics is essential for understanding the multi-molecular complexes at subcellular levels. Fluorescent Protein (XFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells, unraveling the live states of the matter. Original image analysis methods are then required to process challenging 2D or 3D image sequences. Recently, tracking methods that estimate the whole trajectories of moving objects have been successfully developed. In this paper, we address rather the detection of meaningful events in spatio-temporal fluorescence image sequences, such as apparent stable "stocking areas" involved in membrane transport. We propose an original patch-based Markov modeling to detect spatial irregularities in fluorescence images with low false alarm rates. This approach has been developed for real image sequences of cells expressing XFP-tagged Rab proteins, known to regulate membrane trafficking.

show abstract

Rao-Blackwellized Marginal Particle Filtering for Multiple Object Tracking in Molecular Bioimaging

Cited by 20 publications

References 14 publications

Cellular Tracking in Time-Lapse Phase Contrast Images

Cellular Tracking in Time-Lapse Phase Contrast Images

An optimal-path approach for neural circuit reconstruction

Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

Contact Info

Product

Resources

About