Name
Title | ||
|---|---|---|
Detection of fungal spores in 3D microscopy images of macroscopic areas of host tissue |
Abstract | ||
|---|---|---|
The measurement of variation in characteristics of an organism is referred to as phenotyping, and using image data to extract phenotypes is a rapidly developing area in biological research. For studying host-pathogen interactions, 3D microscopy data can provide useful information about mechanisms of infection and defense. Performing research on a fungal pathogen of a plant, we recently developed methods to image and combine multiple fields of view of microscopy data across a macroscopic scale. This study was focused on using macroscopic microscopy data to digitally extract the top epidermal cell layer of plant leaves and to count the number of fungal spores on the epidermis. This was achieved using an active surface approach to estimate the 3D position of the epidermis and a shape-template matching approach to detect spores. A compact shape representation is proposed to model spore shapes and generate candidate templates for detecting spores. Our experiments show results that indicate strong promise for the proposed approach in studying plant-fungal interactions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BIBM.2016.7822564 | 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
Spore Detection,Confocal Microscopy,Macroscopic Microscopy | Computer vision,Biological system,Biology,Spore,Artificial intelligence,Microscopy,Macroscopic scale,Machine learning | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-5090-1612-9 | 1 |
PageRank | References | Authors |
0.48 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhishek Kolagunda | 1 | 24 | 6.28 |
| Randall Wisser | 2 | 1 | 1.83 |
| Timothy Chaya | 3 | 1 | 0.82 |
| Jeffrey Caplan | 4 | 1 | 0.82 |
| Chandra Kambhamettu | 5 | 858 | 80.83 |