title: Dielectrophoresis-based classification of cells using multi-target multiple-hypothesis tracking.
abstract: In this paper we present a novel methodology for classifying cells by using a combination of dielectrophoresis, image tracking and classification algorithms. We use dielectrophoresis to induce unique motion patterns in cells of interest. Motion is extracted via multi-target multiple-hypothesis tracking. Trajectories are then used to classify cells based on a generalized likelihood ratio test. We present results of a simulation study and of our prototype tracking the dielectrophoretic velocities of cells.
authors.forename: Samuel J
authors.initials: SJ
authors.lastname: Dickerson
authors.forename: Donald M
authors.initials: DM
authors.lastname: Chiarulli
authors.forename: Steven P
authors.initials: SP
authors.lastname: Levitan
authors.forename: Craig
authors.initials: C
authors.lastname: Carthel
authors.forename: Stefano
authors.initials: S
authors.lastname: Coraluppi
mesh_terms: D000465:Algorithms; D058948:Cell Tracking / Q000379:methods*; D003198:Computer Simulation; D004560:Electricity; D004566:Electrodes; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000166:cytology*