Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography.
We present a new statistical approach to real-time sensing and recognition of microorganisms using digital holographic microscopy. We numerically produce many section images at different depths along a longitudinal direction from the single digital hologram of three-dimensional (3D) microorganisms in the Fresnel domain. For volumetric 3D recognition, the test pixel points are randomly selected from the section image; this procedure can be repeated with different specimens of the same microorganism. The multivariate joint density functions are calculated from the pixel values of each section image at the same random pixel points. The parameters of the statistical distributions are compared using maximum likelihood estimation and statistical inference algorithms. The performance of the proposed system is illustrated with preliminary experimental results.
Moon
Inkyu
I
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, Connecticut 06269-2157, USA
Javidi
Bahram
B
D001185:Artificial Intelligence; D003198:Computer Simulation; D017377:Diatoms / Q000166:cytology*; D006696:Holography / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted