About this Abstract |
Meeting |
5th International Congress on 3D Materials Science (3DMS 2021)
|
Symposium
|
5th International Congress on 3D Materials Science (3DMS 2021)
|
Presentation Title |
Supervised Texture-based Classification for 3DEM |
Author(s) |
Alexander S. Hall, Remi Blanc, Jeffrey Caplan, Madesh Muniswamy |
On-Site Speaker (Planned) |
Alexander S. Hall |
Abstract Scope |
Dense 3DEM data recovered by Serial Block Face Imaging (SBFI), Serial Section Transmission Electron Microscopy (ssTEM), and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) present challenging image segmentation needs. Traditional image processing may struggle to faithfully follow features. The variance in electron density between materials provides ample signal for machine learning methods, though. Here, we demonstrate supervised image classification based on image texture as implemented in Amira-Avizo Software. One provides training data from manual segmentation or automated methods. The program then trains a classifier using two statistical categories: features based on co-occurrence matrices and features based on intensity statistics. We tested our implementation on a 10 Gb SBFI mouse heart tissue block collected using an Apreo VolumeScope SEM. When followed by a small amount of image processing, the texture-supervised classification was able to fully label this data set with less than 10 minutes of user effort. |
Proceedings Inclusion? |
Definite: Other |