Abstract
High-throughput microscopy allows researchers to produce large volumetric images of biological tissue at sub-micrometer resolution. Serial electron microscopy (EM) has the ability to improve three-dimensional imaging dramatically by providing nanometer-scale resolution. Serial EM data sets of brain tissue can potentially be used to reconstruct the complex structure of biological neural networks. These data sets consist of gigabytes of volumetric data densely packed with anatomical information. This makes three-dimensional EM data sets difficult to visualize. In this paper, we present new methods for visualizing EM data sets using a novel transfer function based on the local variance of volumetric features. We first construct a tensor field that describes the local shape and orientation of structures. These tensors are then used to visualize anatomical features such as cell bodies, membranes, and fibers. We do this by using the tensor shape and orientation to design transfer functions that allow selective visualization of features in dense EM image stacks.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings |
| Pages | 9-16 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2012 |
| Event | 2nd IEEE Symposium on Biological Data Visualization, BioVis 2012 - Seattle, WA, United States Duration: Oct 14 2012 → Oct 19 2012 |
Publication series
| Name | IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings |
|---|
Other
| Other | 2nd IEEE Symposium on Biological Data Visualization, BioVis 2012 |
|---|---|
| Country | United States |
| City | Seattle, WA |
| Period | 10/14/12 → 10/19/12 |
Fingerprint
Keywords
- I.4.10 [Image Processing and Computer Vision]: Image Representation - Volumetric
- I.5.1 [Pattern Recognition]: Models - Statistical
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Bioengineering
Cite this
Volume visualization in serial electron microscopy using local variance. / Mayerich, David; Hart, John C.
IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings. 2012. p. 9-16 6378578 (IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Volume visualization in serial electron microscopy using local variance
AU - Mayerich, David
AU - Hart, John C
PY - 2012/12/1
Y1 - 2012/12/1
N2 - High-throughput microscopy allows researchers to produce large volumetric images of biological tissue at sub-micrometer resolution. Serial electron microscopy (EM) has the ability to improve three-dimensional imaging dramatically by providing nanometer-scale resolution. Serial EM data sets of brain tissue can potentially be used to reconstruct the complex structure of biological neural networks. These data sets consist of gigabytes of volumetric data densely packed with anatomical information. This makes three-dimensional EM data sets difficult to visualize. In this paper, we present new methods for visualizing EM data sets using a novel transfer function based on the local variance of volumetric features. We first construct a tensor field that describes the local shape and orientation of structures. These tensors are then used to visualize anatomical features such as cell bodies, membranes, and fibers. We do this by using the tensor shape and orientation to design transfer functions that allow selective visualization of features in dense EM image stacks.
AB - High-throughput microscopy allows researchers to produce large volumetric images of biological tissue at sub-micrometer resolution. Serial electron microscopy (EM) has the ability to improve three-dimensional imaging dramatically by providing nanometer-scale resolution. Serial EM data sets of brain tissue can potentially be used to reconstruct the complex structure of biological neural networks. These data sets consist of gigabytes of volumetric data densely packed with anatomical information. This makes three-dimensional EM data sets difficult to visualize. In this paper, we present new methods for visualizing EM data sets using a novel transfer function based on the local variance of volumetric features. We first construct a tensor field that describes the local shape and orientation of structures. These tensors are then used to visualize anatomical features such as cell bodies, membranes, and fibers. We do this by using the tensor shape and orientation to design transfer functions that allow selective visualization of features in dense EM image stacks.
KW - I.4.10 [Image Processing and Computer Vision]: Image Representation - Volumetric
KW - I.5.1 [Pattern Recognition]: Models - Statistical
UR - http://www.scopus.com/inward/record.url?scp=84872243667&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872243667&partnerID=8YFLogxK
U2 - 10.1109/BioVis.2012.6378578
DO - 10.1109/BioVis.2012.6378578
M3 - Conference contribution
AN - SCOPUS:84872243667
SN - 9781467347303
T3 - IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings
SP - 9
EP - 16
BT - IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings
ER -