A simultaneous localization and tracking method for a worm tracking system

Mateusz Kowalski; Piotr Kaczmarek; Rafał Kabaciński; Mieszko Matuszczak; Kamil Tranbowicz; Robert Sobkowiak

International Journal of Applied Mathematics and Computer Science (2014)

  • Volume: 24, Issue: 3, page 599-609
  • ISSN: 1641-876X

Abstract

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The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.

How to cite

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Mateusz Kowalski, et al. "A simultaneous localization and tracking method for a worm tracking system." International Journal of Applied Mathematics and Computer Science 24.3 (2014): 599-609. <http://eudml.org/doc/271901>.

@article{MateuszKowalski2014,
abstract = {The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.},
author = {Mateusz Kowalski, Piotr Kaczmarek, Rafał Kabaciński, Mieszko Matuszczak, Kamil Tranbowicz, Robert Sobkowiak},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {Caenorhabditis elegans behavior; worm tracking; computer vision; image processing; feature extraction},
language = {eng},
number = {3},
pages = {599-609},
title = {A simultaneous localization and tracking method for a worm tracking system},
url = {http://eudml.org/doc/271901},
volume = {24},
year = {2014},
}

TY - JOUR
AU - Mateusz Kowalski
AU - Piotr Kaczmarek
AU - Rafał Kabaciński
AU - Mieszko Matuszczak
AU - Kamil Tranbowicz
AU - Robert Sobkowiak
TI - A simultaneous localization and tracking method for a worm tracking system
JO - International Journal of Applied Mathematics and Computer Science
PY - 2014
VL - 24
IS - 3
SP - 599
EP - 609
AB - The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.
LA - eng
KW - Caenorhabditis elegans behavior; worm tracking; computer vision; image processing; feature extraction
UR - http://eudml.org/doc/271901
ER -

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