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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gang Lin Al Kofahi, Y. Tyrrell, J.A. Bjornsson, C. Shain, W. Roysam, B. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Electr., Comput., & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY (Gang Lin; Al Kofahi, Y.; Tyrrell, J.A.) |
| Abstract | Brain tissue is the most complex tissue in the mammalian anatomy. It is structurally complex, containing diverse cell types and vasculature with complex spatial relationships. It is also complex in terms of molecular constituents, and their relationship to the structural constituents. There is a need for technologies to map normal and injured brain tissue and compute appropriate quantitative measurements in support of a large number of investigations. We present a systematic methodology named FARSIGHT (fluorescence association rules for image-based insight), combining multi-parameter immuno-histochemical labeling, three-dimensional confocal microscopy, linear unmixing, segmentation, and associative morphometry to map and quantify diverse aspects of brain tissue. In addition to morphometric measurements of individual structures from individual imaging channels, we propose the use of distance maps, distance-weighted integrals, and distributions as an efficient basis for computing a versatile set of such associative measurements. These features enable classification of all the major cell types, and computation of their inter-relationships. |
| Starting Page | 1040 |
| Ending Page | 1043 |
| File Size | 942623 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424406714 |
| DOI | 10.1109/ISBI.2007.357033 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Brain Microscopy Anatomy Appropriate technology Fluorescence Association rules Diversity reception Labeling Image segmentation Distributed computing |
| Content Type | Text |
| Resource Type | Article |
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