A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME
Automated tracking of fluorescent particles in living cells is vital for subcellular stoichoimetry analysis [1, 2]. Here, a new automatic tracking algorithm is described to track multiple particles, based on minimal path optimization. After linking feature points frame-by-frame, spatio-temporal data...
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| Format: | Conference item |
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2009
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| _version_ | 1826288448501186560 |
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| author | Xue, Q Leake, M IEEE |
| author_facet | Xue, Q Leake, M IEEE |
| author_sort | Xue, Q |
| collection | OXFORD |
| description | Automated tracking of fluorescent particles in living cells is vital for subcellular stoichoimetry analysis [1, 2]. Here, a new automatic tracking algorithm is described to track multiple particles, based on minimal path optimization. After linking feature points frame-by-frame, spatio-temporal data from time-lapse microscopy are combined together to construct a transformed 3D volume. The trajectories are then generated from the minimal energy path as defined by the solution of the time-dependent partial differential equation using a gray weighted distance transform dynamic programming method. Results from simulated and experimental data demonstrate that our novel automatic method gives sub-pixel accuracy even for very noisy images. © 2009 IEEE. |
| first_indexed | 2024-03-07T02:13:51Z |
| format | Conference item |
| id | oxford-uuid:a18eebf1-3c73-4181-a0a2-e8bcec2f145d |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:13:51Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:a18eebf1-3c73-4181-a0a2-e8bcec2f145d2022-03-27T02:14:03ZA NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUMEConference itemhttp://purl.org/coar/resource_type/c_5794uuid:a18eebf1-3c73-4181-a0a2-e8bcec2f145dSymplectic Elements at Oxford2009Xue, QLeake, MIEEEAutomated tracking of fluorescent particles in living cells is vital for subcellular stoichoimetry analysis [1, 2]. Here, a new automatic tracking algorithm is described to track multiple particles, based on minimal path optimization. After linking feature points frame-by-frame, spatio-temporal data from time-lapse microscopy are combined together to construct a transformed 3D volume. The trajectories are then generated from the minimal energy path as defined by the solution of the time-dependent partial differential equation using a gray weighted distance transform dynamic programming method. Results from simulated and experimental data demonstrate that our novel automatic method gives sub-pixel accuracy even for very noisy images. © 2009 IEEE. |
| spellingShingle | Xue, Q Leake, M IEEE A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME |
| title | A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME |
| title_full | A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME |
| title_fullStr | A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME |
| title_full_unstemmed | A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME |
| title_short | A NOVEL MULTIPLE PARTICLE TRACKING ALGORITHM FOR NOISY IN VIVO DATA BY MINIMAL PATH OPTIMIZATION WITHIN THE SPATIO-TEMPORAL VOLUME |
| title_sort | novel multiple particle tracking algorithm for noisy in vivo data by minimal path optimization within the spatio temporal volume |
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