Wavelets analyze cells without reagents

Cell viability is one of the basic properties indicating the physiological state of the cell, thus, has long been one of the major considerations in biotech applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable but some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed based on supervised learning technique that learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. For more information, go to: http://www.biomedcentral.com/content/pdf/1471-2105-9-449.pdf