Understanding what we cannot see: automatic analysis of 4d digital in-line holographic microscopy data

Authors:
Laura Leal-Taixé;Matthias Heydt;Axel Rosenhahn;Bodo Rosenhahn
Affiliations:
Leibniz Universität Hannover, Hannover, Germany;Applied Physical Chemistry, University of Heidelberg, Heidelberg, Germany;Applied Physical Chemistry, University of Heidelberg, Heidelberg, Germany;Leibniz Universität Hannover, Hannover, Germany
Venue:
Proceedings of the 2010 international conference on Video Processing and Computational Video
Year:
2010

Citing 7
Cited 0

Discovery and Segmentation of Activities in Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision

Scale-Space Theory in Computer Vision
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network

IEEE Transactions on Pattern Analysis and Machine Intelligence
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Target Tracking - Linking Identities using Bayesian Network Inference

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Classification of swimming microorganisms motion patterns in 4D digital in-line holography data

Proceedings of the 32nd DAGM conference on Pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Digital in-line holography is a microscopy technique which got an increasing attention over the last few years in the fields of microbiology, medicine and physics, as it provides an efficient way of measuring 3D microscopic data over time. In this paper, we present a complete system for the automatic analysis of digital in-line holographic data; we detect the 3D positions of the microorganisms, compute their trajectories over time and finally classify these trajectories according to their motion patterns. Tracking is performed using a robust method which evolves from the Hungarian bipartite weighted graph matching algorithm and allows us to deal with newly entering and leaving particles and compensate for missing data and outliers. In order to fully understand the behavior of the microorganisms, we make use of Hidden Markov Models (HMMs) to classify four different motion patterns of a microorganism and to separate multiple patterns occurring within a trajectory. We present a complete set of experiments which show that our tracking method has an accuracy between 76% and 91%, compared to ground truth data. The obtained classification rates on four full sequences (2500 frames) range between 83.5% and 100%.