title: Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering.
abstract: We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell line were used in the evaluation for a total number of more than 3500 cells. Besides real images, simulated images were also used in the evaluation. The detection error was between approximately zero and 15.5% which is a significantly superior performance compared to baseline approaches.
authors.forename: Firas
authors.initials: F
authors.lastname: Mualla
authors.forename: Simon
authors.initials: S
authors.lastname: Scholl
authors.forename: Bjorn
authors.initials: B
authors.lastname: Sommerfeldt
authors.forename: Andreas
authors.initials: A
authors.lastname: Maier
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger