Applying Pattern Recognition to High-Resolution Images to Determine Cellular Signaling Status.

ABSTRACT: Two frequently used tools to acquire high- resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and living cells. In current practice, these images are viewed by eye to determine cellular status, e.g., activated versus resting. Automatic and quantitative data analysis is lacking. This paper develops an algorithm of pattern recognition that works very effectively for AFM and SEM images. Using rat basophilic leukemia cells, our approach creates a support vector machine to automatically classify resting and activated cells. Ten-fold cross-validation with cells that are known to be activated or resti

SUBMITTER: Lohrer MF 

PROVIDER: S-EPMC5633003 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature