title: Automated analysis and classification of infected macrophages using bright-field amplitude contrast data.
abstract: This article presents a methodology for acquisition and analysis of bright-field amplitude contrast image data in high-throughput screening (HTS) for the measurement of cell density, cell viability, and classification of individual cells into phenotypic classes. We present a robust image analysis pipeline, where the original data are subjected to image standardization, image enhancement, and segmentation by region growing. This work develops new imaging and analysis techniques for cell analysis in HTS and successfully addresses a particular need for direct measurement of cell density and other features without using dyes.
authors: UES, Inc., Dayton, OH, USA.
authors: Umesh
authors: U
authors: Adiga
authors: Debbie
authors: D
authors: Taylor
authors: Brian
authors: B
authors: Bell
authors: Larissa
authors: L
authors: Ponomareva
authors: Stephen
authors: S
authors: Kanzlemar
authors: Ryan
authors: R
authors: Kramer
authors: Roland
authors: R
authors: Saldanha
authors: Sandra
authors: S
authors: Nelson
authors: Thomas J
authors: TJ
authors: Lamkin
mesh_terms: D000465:Algorithms; D002452:Cell Count; D002470:Cell Survival; D005604:Francisella tularensis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008264:Macrophages / Q000382:microbiology*; D008853:Microscopy / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype; D014406:Tularemia / Q000175:diagnosis*