Abstract
Nanodiscs are soluble nanoscale phospholipid bilayers with applications in drug delivery and the study of membrane proteins, for example. They can be imaged using electron microscopy, along with immunogold markers indicating locations of proteins of interest. We describe and evaluate methods for automatically detecting and segmenting nanodiscs in electron micrographs. The detection method modifies aspects of the Fast Radial Symmetry Transform to detect nanodiscs that exhibit approximate radial symmetry against noisy but predominantly lighter background. Detected nanodiscs are then segmented using radial active contours. Experiments on micrographs both with and without immunogold markers indicate promising detection and segmentation performance, and that information on nanodisc quantities, locations, size distributions, and co-location with proteins of interest could be extracted automatically.
Original language | English |
---|---|
Title of host publication | Medical Image Understanding and Analysis |
Subtitle of host publication | MIUA |
Editors | Constantino Reyes-Aldasoro, Greg Slabaugh |
Place of Publication | United Kingdom |
Publisher | British Machine Vision Association and Society for Pattern Recognition |
Pages | 53-58 |
Number of pages | 6 |
ISBN (Print) | 1901725510 |
Publication status | Published - Jul 2014 |
Event | 18th Annual Conference in Medical Image Understanding and Analysis - Moore Complex, Royal Holloway, London, United Kingdom Duration: 9 Jul 2014 → 11 Jul 2014 http://www.city.ac.uk/medical-image-understanding-and-analysis-2014 |
Conference
Conference | 18th Annual Conference in Medical Image Understanding and Analysis |
---|---|
Abbreviated title | MIUA 2014 |
Country/Territory | United Kingdom |
City | London |
Period | 9/07/14 → 11/07/14 |
Internet address |
Fingerprint
Dive into the research topics of 'Detecting and Segmenting Nanodiscs in Immuno-Electron Micrographs'. Together they form a unique fingerprint.Student theses
-
Sequential Recognition of Manipulation Actions Using Superpixel Group Mining
Author: Huang, T., 2019Supervisor: McKenna, S. (Supervisor) & Zhang, J. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy
File