Video Object Tracking in Neural Axons with Fluorescence Microscopy Images
Neurofilament is an important type of intercellular cargos transmitted in neural axons. Given fluorescence microscopy images, existing methods extract neurofilament movement patterns by manual tracking. In this paper, we describe two automated tracking methods for analyzing neurofilament movement ba...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/423876 |
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| _version_ | 1849305469595680768 |
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| author | Liang Yuan Junda Zhu |
| author_facet | Liang Yuan Junda Zhu |
| author_sort | Liang Yuan |
| collection | DOAJ |
| description | Neurofilament is an important type of intercellular cargos transmitted in neural axons. Given fluorescence microscopy images, existing methods extract neurofilament movement patterns by manual
tracking. In this paper, we describe two automated tracking methods for analyzing neurofilament movement based on two different techniques: constrained particle filtering and tracking-by-detection. First, we introduce the constrained particle filtering approach. In this approach, the orientation and position of a particle are constrained by the axon’s shape such that fewer particles are necessary for tracking neurofilament movement than object tracking techniques based on generic particle filtering. Secondly, a tracking-by-detection approach to neurofilament tracking is presented. For this approach, the axon is decomposed into blocks, and the blocks encompassing the moving neurofilaments are detected by graph labeling using Markov random field. Finally, we compare two tracking methods by performing tracking experiments on real time-lapse image sequences of neurofilament movement, and the experimental results show that both methods demonstrate good performance in comparison with the existing approaches, and the tracking accuracy of the tracing-by-detection approach is slightly better between the two. |
| format | Article |
| id | doaj-art-0a28c8bcd8c4456bb835c91efc71de9d |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-0a28c8bcd8c4456bb835c91efc71de9d2025-08-20T03:55:27ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/423876423876Video Object Tracking in Neural Axons with Fluorescence Microscopy ImagesLiang Yuan0Junda Zhu1School of Mechanical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, ChinaDepartment of Electrical and Computer Engineering, University of Macau, MacauNeurofilament is an important type of intercellular cargos transmitted in neural axons. Given fluorescence microscopy images, existing methods extract neurofilament movement patterns by manual tracking. In this paper, we describe two automated tracking methods for analyzing neurofilament movement based on two different techniques: constrained particle filtering and tracking-by-detection. First, we introduce the constrained particle filtering approach. In this approach, the orientation and position of a particle are constrained by the axon’s shape such that fewer particles are necessary for tracking neurofilament movement than object tracking techniques based on generic particle filtering. Secondly, a tracking-by-detection approach to neurofilament tracking is presented. For this approach, the axon is decomposed into blocks, and the blocks encompassing the moving neurofilaments are detected by graph labeling using Markov random field. Finally, we compare two tracking methods by performing tracking experiments on real time-lapse image sequences of neurofilament movement, and the experimental results show that both methods demonstrate good performance in comparison with the existing approaches, and the tracking accuracy of the tracing-by-detection approach is slightly better between the two.http://dx.doi.org/10.1155/2014/423876 |
| spellingShingle | Liang Yuan Junda Zhu Video Object Tracking in Neural Axons with Fluorescence Microscopy Images Journal of Applied Mathematics |
| title | Video Object Tracking in Neural Axons with Fluorescence Microscopy Images |
| title_full | Video Object Tracking in Neural Axons with Fluorescence Microscopy Images |
| title_fullStr | Video Object Tracking in Neural Axons with Fluorescence Microscopy Images |
| title_full_unstemmed | Video Object Tracking in Neural Axons with Fluorescence Microscopy Images |
| title_short | Video Object Tracking in Neural Axons with Fluorescence Microscopy Images |
| title_sort | video object tracking in neural axons with fluorescence microscopy images |
| url | http://dx.doi.org/10.1155/2014/423876 |
| work_keys_str_mv | AT liangyuan videoobjecttrackinginneuralaxonswithfluorescencemicroscopyimages AT jundazhu videoobjecttrackinginneuralaxonswithfluorescencemicroscopyimages |