Name
Abstract | ||
|---|---|---|
Scanning X-ray microdiffraction (SXMD) is a novel technique to study the macromolecular architecture of tissues, such as cellulose in biomass. SXMD can generate huge amount of scattering patterns corresponding to different positions on a sample. In this paper, 190 images in a 38 x 5 grid are collected from SXMD experiment done at APS in Argonne National Lab to study nanoscale architecture in plant cell wall. A pattern-partition strategy utilizing image entropy, similarity coefficient analysis and k-means based clustering was carried out to study these diffraction patterns. Both similarity coefficient analysis and k-means clustering provide informative results in regard of the nanoscale architecture of Arabidopsis stem. This strategy is shown to reduce the amount of pre-processing work needed to analyze SXMD data. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS) | Scanning X-ray microdiffraction,Entropy,Correlation,Clustering |
Field | DocType | ISSN |
Computer vision,Nanoscopic scale,X-ray,Biological system,Computer science,Optics,Scattering,Artificial intelligence,Cluster analysis,Diffraction,Grid | Conference | 2163-4025 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yan zhang | 1 | 67 | 20.55 |
| jiliang liu | 2 | 0 | 0.34 |
| Lee Makowski | 3 | 2 | 2.60 |