title: 3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging.
abstract: We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-line (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms.
authors.affiliation: Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269 USA. inkyu.moon@uconn.edu
authors.forename: Inkyu
authors.initials: I
authors.lastname: Moon
authors.forename: Bahram
authors.initials: B
authors.lastname: Javidi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002477:Cells / Q000166:cytology; D003198:Computer Simulation; D006696:Holography / Q000295:instrumentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D008858:Microscopy, Phase-Contrast; D008954:Models, Biological; D015999:Multivariate Analysis; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D059828:Plant Cells; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation