NANOMET: GATEWAY TO INTEGRATED IMAGE UTILIZATION
Your images are data– especially the bad ones.
Our natural tendency is to keep our good images and discard our bad ones. The quality images go on to publications and particle and fiber metrology, while the others disappear. There is a tremendous loss of information in such an approach, since all of our imaging failures contain vital information about particle aggregation, contaminant phases, poor choices of surfactants, failed sample preparation, and microscope performance. In the absence of high throughput automated image analysis, such processing of our imaging failures was cost and time prohibitive.
NanoMet changes everything.
NanoMet integrates the images themselves as well as all of the associated object metrics into a relational database, opening the door for integrated and distributed image utilization. Queries can be generated to identify similar images based any and all aspect of particle metrology: the number of particles per unit area, mean and variance of particle diameter, and advanced statistical metrics like size distribution extrema, skewness, etc. NanoMet Custom Solutions allows these metrics to be extended beyond particle diameter and fiber width to such metrics as aspect ratio, particle area/volume, fiber length, and more.
The NanoMet database structure is immediately scalable to include new user-defined meta-tags, which allow each image processed by NanoMet to be stored and associated with
not only every dimensional metric NanoMet provides through its high throughput metrology engine, but such information as material synthesis parameters, data from other non-
imaging techniques, and sample preparation protocol data.
Particle size metrics are not the only useful measures in correlating images with each other and with user defined meta-tags. NanoMet
Custom Solutions allows the user to use the NanoMet high throughput engine to measure and correlate such parameters as the maximum contrast density in an image, the total image material coverage, the number of edges present in an image, etc. This allows NanoMet to deeply mine information from images and correlate them with particle size metrics, sample synthesis parameters, and even user defined image quality marks such as: this image contained goethite needles. With this power, you’ll be sure to process all your images through NanoMet not only to dimension those materials, but correlate what you find with
material synthesis meta-data as well as sample preparation protocols. More importantly, this data mining can be applied across the history of your R&D, constrained to specific materials processes, or correlated across a series of parallel processes.