Next Article in Journal
A High-Sensitivity Hydraulic Load Cell for Small Kitchen Appliances
Next Article in Special Issue
Wearable Systems for Monitoring Mobility-Related Activities in Chronic Disease: A Systematic Review
Previous Article in Journal
Combined Simulation of a Micro Permanent Magnetic Linear Contactless Displacement Sensor
Previous Article in Special Issue
A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Article Menu

Export Article

Open AccessReview
Sensors 2010, 10(9), 8437-8451; doi:10.3390/s100908437

Automated Three-Dimensional Microbial Sensing and Recognition Using Digital Holography and Statistical Sampling

1
School of Computer Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759 South Korea
2
Department of Electrical and Computer Engineering, U-2157, University of Connecticut, Storrs, CT 06269-2157, USA
*
Author to whom correspondence should be addressed.
Received: 18 June 2010 / Revised: 30 August 2010 / Accepted: 3 September 2010 / Published: 9 September 2010
(This article belongs to the Special Issue Sensors in Biomechanics and Biomedicine)
View Full-Text   |   Download PDF [852 KB, uploaded 21 June 2014]   |  

Abstract

We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition.
Keywords: digital holography; 3D microscopy; cell analysis; statistical pattern recognition; medical imaging; bio-sensing digital holography; 3D microscopy; cell analysis; statistical pattern recognition; medical imaging; bio-sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Moon, I.; Yi, F.; Javidi, B. Automated Three-Dimensional Microbial Sensing and Recognition Using Digital Holography and Statistical Sampling. Sensors 2010, 10, 8437-8451.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top