Tracking
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==Overview== | ==Overview== | ||
| − | 5-D Image analysis involves object extraction and tracking of multiple channel data. A typical dataset is shown on the right, with two types of T-cells that need to be tracked. It also has dendritic cells and blood vessels imaged in the same spatial context. The main framework for analyzing such a movie has been described in [[FARSIGHT_Framework]]. In | + | 5-D Image analysis involves object extraction and tracking of multiple channel data. A typical dataset is shown on the right, with two types of T-cells that need to be tracked. It also has dendritic cells and blood vessels imaged in the same spatial context. The main framework for analyzing such a movie has been described in [[FARSIGHT_Framework]]. In a nut shell, we adopt a divide and conquer strategy in segmenting the different channels. We then track each channel individually and computer both intrinsic features, which are intrinsic to an object and associative features between an object and other channels. |
[[Image:5D_sample_ena.png|500px|thumb|(left)A sample 2-D projection of a single time snapshot from a 5-D movie from the immune system. (right)The corresponding tracking output]] | [[Image:5D_sample_ena.png|500px|thumb|(left)A sample 2-D projection of a single time snapshot from a 5-D movie from the immune system. (right)The corresponding tracking output]] | ||
Revision as of 23:53, 8 May 2009
Contents |
5-D Image Analysis
Overview
5-D Image analysis involves object extraction and tracking of multiple channel data. A typical dataset is shown on the right, with two types of T-cells that need to be tracked. It also has dendritic cells and blood vessels imaged in the same spatial context. The main framework for analyzing such a movie has been described in FARSIGHT_Framework. In a nut shell, we adopt a divide and conquer strategy in segmenting the different channels. We then track each channel individually and computer both intrinsic features, which are intrinsic to an object and associative features between an object and other channels.
Tracking Program
Usage:
track
Input parameters are contained in the file filename.conf located in the same directory of execution. It contains one row per 3-D image to be processed of the form
DatasetID "Filename" channel_id time_point
An example file look like the following lines
dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w1_t10.tif" 1 10 dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w2_t10.tif" 2 10 dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w3_t10.tif" 3 10 dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w4_t10.tif" 4 10 dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w1_t11.tif" 1 11 dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w2_t11.tif" 2 11 dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w3_t11.tif" 3 11 ... dataset1 "data/wF5p120307m1s5-t10/wF5p120307m1s5_w4_t14.tif" 4 14
First line represents one 3-D image of a movie named "dataset1" with filename "data/wF5p120307m1s5-t10/wF5p120307m1s5_w1_t10.tif". This file is from channel 1 and time point 10.
Tracking Editor
Authors
The following people are involved in this project
