A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selec

Background: Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological 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 and versatile, however, 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. Results: This system is based on supervised learning technique. It 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. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algo...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Ning Wei, Erwin Flaschel, Karl Friehs, Tim W. Nattkemper
• Keywords: BMCBI 2008, Cell Viability, Machine Vision, Wavelet