We're Moving to
The content you are trying to access has moved to new site. Explore, discover (and in certain contries, buy) our products and services on our new website. Click to visit now or you will be automatically redirected.
to view non-migrated solutions
Machine learning techniques enable supervised and unsupervised feature selection and clustering of highly multi-parametric data sets. PerkinElmer offers a range of easy to use machine learning tools for its Harmony, Columbus and High Content Profiler products.
The PhenoLOGIC™ plug-in provides supervised machine learning for Harmony® High Content Imaging and Analysis Software and Columbus™ Image Data Management and Analysis System.
PhenoLOGIC enables biologists using Harmony to train the software to develop the image analysis algorithms. While other systems may require an image analysis expert to create an algorithm, PhenoLOGIC uses proprietary machine-learning technology to make it easy for you to do it on your own. Using a learn-by-example approach, images can be segmented with just a few clicks of the mouse and then tailored algorithms developed quickly and easily.
The PhenoLOGIC machine-learning option enables our Harmony High Content Analysis software to recognize different cell populations and regions using a simple learn-by-example approach. Just click on a few cells of each class to teach the software what it is looking for, and PhenoLOGIC will do the rest. Image analysis that might have taken hours can be completed in a matter of minutes.
Using advanced proprietary machine-learning technology, PhenoLOGIC sets parameters for optimal image segmentation and cell classification. The software combines the most meaningful parameters, whether it's two, three, four or more, to achieve accurate classification of cells. The result is highly robust and statistically relevant results.
For Research Use Only. Not for Use in Diagnostic Procedures.
