PPF - A Parallel Particle Filtering Library

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data Fusion & Target Tracking Conference 201

Particle Filtering Methods for Subcellular Motion Analysis

Advances in fluorescent probing and microscopic imaging technology have revolutionized biology in the past decade and have opened the door for studying subcellular dynamical processes. However, accurate and reproducible methods for processing and analyzing the images acquired for such studies are still lacking. Since manual image analysis is time consuming, potentially inaccurate, and poorly reproducible, many biologically highly relevant questions are either left unaddressed, or are answered with great uncertainty. The subject of this thesis is particle filtering methods and their application for multiple object tracking in different biological imaging applications. Particle filtering is a technique for implementing recursive Bayesian filtering by Monte Carlo sampling. A fundamental concept behind the Bayesian approach for performing inference is the possibility to encode the information about the imaging system, possible noise sources, and the system dynamics in terms of probability density functions. In this thesis, a set of novel PF based metho

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Estimation of the flow of particles within a partition of the image domain in fluorescence video-microscopy

International audienceAutomatic analysis of the dynamic content in fluorescence video-microscopy is crucial for understanding molecular mechanisms involved in cell functions. In this paper, we propose an original approach for analyzing particle trafficking in these sequences. Instead of individually tracking every particle, we estimate the particle flows between predefined regions. This approach allows us to process image sequences with a high number of particles and a low frame rate. We investigate several ways to estimate the particle flow at the cellular level and evaluate their performance in synthetic and real image sequences

Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

International audienceThe 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

Tracking Growing Axons by Particle Filtering in 3D+t Fluorescent Two-Photon Microscopy Images

International audienceAnalyzing the behavior of axons in the developing nervous systems is essential for biologists to understand the biological mechanisms underlying how growing axons reach their target cells. The analysis of the motion patterns of growing axons requires detecting axonal tips and tracking their trajectories within complex and large data sets. When performed manually, the tracking task is arduous and time-consuming. To this end, we propose a tracking method, based on the particle filtering technique, to follow the traces of axonal tips that appear as small bright spots in the 3D+t fluorescent two-photon microscopy images exhibiting low signal-to-noise ratios (SNR) and complex background. The proposed tracking method uses multiple dynamic models in the proposal distribution to predict the positions of the growing axons. Furthermore, it incorporates object appearance, motion characteristics of the growing axons, and filament information in the computation of the observation model. The integration of these three sources prevents the tracker from being distracted by other objects that have appearances similar to the tracked objects, resulting in improved accuracy of recovered trajectories. The experimental results obtained from the microscopy images show that the proposed method can successfully estimate trajectories of growing axons, demonstrating its effectiveness even under the presence of noise and complex background

Modeling cell migration in quantitative image analysis

Tese de mestrado em Tecnologias da Informação aplicadas às Ciências Biológicas e Médicas, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012All biological phenomena are dynamic and movement is an essential function in cellular systems but their regulation, characteristics and physiological meaning are not fully known. Measurement of the cell movements provides quantitative information that is inevitable for understanding the cellular system. Cell migration is a field of intense current research generating high amounts of image data that need to be quantitatively analyzed with efficiency, consistency and completeness. To accomplish, computerized motion analysis is rapidly becoming a requisite. Since all the existing algorithms for these purposes are often not robust, effective and optimal enough to yield satisfactory results, new and alternative methods must be developed. The aim of this work is to find and develop an alternative to the tracking of individual cells in order to, visualize, characterize and quantify the migration characteristics of cell population. This alternative comprises the implementation of a simple and automated algorithm to obtain qualitative and quantitative information from image sequences of cell migration in a fast, easy and inexpensive computationally way. After an extensive literature review, it became clear that all the methodologies and approaches employed to make the quantitative analysis of cell migration only presented solutions that involved object tracking. And the new method developed estimates the probability density functions for cell migration and was implemented as a plugin (Migration) for ImageJ, as cross platform open source application. In the evaluation of the developed algorithm was taken in to account his applicability, efficiency, consistency, completeness and validity. It can be used to in image sequences to extract information regarding the distribution of the future positions of all particles in a determined time point in the future and is quick when is executing. The results obtained with this method were satisfactory. Comparing to existing approaches to study the cell migration this method adds an improvement, it can deal with complex situation, such as overlapping of particles or other occlusions.Todos os fenómenos biológicos são dinâmicos e o movimento é uma função essencial nos sistemas celulares, mas a sua regulação, características e significado fisiológico não são totalmente conhecidos. A medição dos movimentos das células providencia informação quantitativa para compreender o sistema celular. A migração de células é um campo de intensa investigação gerando grandes quantidades de dados que necessitam de ser quantitativamente analisados com eficiência, consistência e de maneira completa. Para tal, a análise do movimento através dos sistemas de informação está a tornar-se cada vez mais num requisito. Dado que os algoritmos disponíveis para este propósito não são muitas vezes robustos, eficientes e óptimos para proporcionarem resultados satisfatórios, métodos alternativos devem ser desenvolvidos e implementados. O objectivo deste trabalho é encontrar e desenvolver uma alternativa para o tracking de células de modo a se visualizar, caracterizar e quantificar a migração de células. Esta alternativa requer a implementação de um algoritmo simples e automático para obter a informação, quer qualitativa, quer quantitativa de um vídeo, com imagens da migração de células, de um modo rápido e fácil. Depois de uma revisão bibliográfica extensa, verificou-se que todos os métodos implementados para fazer a análise quantitativa da migração de células eram soluções de tracking de partículas. O novo método aqui desenvolvido estima as funções de densidade de probabilidade para a migração de células e foi implementado como um plugin (Migration) para o ImageJ. A avaliação do algoritmo desenvolvido teve em conta a sua aplicabilidade, eficiência, consistência e validade. Pode ser usado em vídeos e extrair informação relativa à estimação da distribuição das posições de todas as partículas num determinado momento no tempo, executando de maneira rápida. Todos os resultados obtidos com este novo método são satisfatórios. Comparando com as abordagens conhecidas da literatura, este método apresenta uma melhoria, pode lidar com situações complexas, tais como sobreposição de partículas e outras oclusões

NETWORK TOMOGRAPHY AND MINIMAL PATHS FOR TRAFFIC FLOWESTIMATION IN MOLECULAR IMAGING

International audienceGreen Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about molecule transport, that is about traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the recent advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real image sequences and Rab proteins