Automated classification and recognition of bacterial particles in flow by multi-angle scatter measurement and a support-vector machine classifier

Bartek Rajwa; Murugesan Venkatapathi; Kathy Ragheb; Padmapriya P. Banada; E. Daniel Hirleman; Todd Lary; J. Paul Robinson

doi:10.1117/12.699227

19 February 2007 Automated classification and recognition of bacterial particles in flow by multi-angle scatter measurement and a support-vector machine classifier

Bartek Rajwa, Murugesan Venkatapathi, Kathy Ragheb, Padmapriya P. Banada, E. Daniel Hirleman, Todd Lary, J. Paul Robinson

Author Affiliations +

Proceedings Volume 6441, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues V; 64410O (2007) https://doi.org/10.1117/12.699227
Event: SPIE BiOS, 2007, San Jose, California, United States

Abstract

Biological microparticles scatter light in all directions when illuminated. The complex scatter pattern is dependent on particle size, shape, refraction index, density, and morphology. Commercial flow cytometers allow measurement at two nominal angles (2°⩽θ₁⩽20° and 70°⩽θ₂⩽110°) of scattered light intensity from individual microparticles with a speed varying from 10 to 10000 particles per second. The choice of angle is dictated by the fact that scattered light in the small-angle region is primarily influenced by cell size and refractive index, whereas side scatter intensity is related to the granularity of cellular structures. These rudimentary measurements cannot be used to separate populations of cells of similar shape, size, or structure. Hence, there have been several attempts in cytometry to measure the entire scatter patterns. However, the published concepts required use of unique custom-built cytometers and could not be applied to existing instruments. The presented work demonstrates application of pattern-recognition techniques to classify particles on the basis of their discrete scatter patterns collected at just five different angles, and accompanied by the measurement of axial light loss. Our approach can be used with existing instruments and requires only the addition of a custom-built scatter-detector. Our analytical model of scatter of laser beams by individual bacterial cells suspended in a fluid was used to determine the location for scatter sensors. Experimental results were used to train the pattern recognition system. It has been shown that information provided just by six scatter-related parameters was sufficient to recognize various bacteria with 90-99% success rate.

Citation Download Citation

Bartek Rajwa, Murugesan Venkatapathi, Kathy Ragheb, Padmapriya P. Banada, E. Daniel Hirleman, Todd Lary, and J. Paul Robinson "Automated classification and recognition of bacterial particles in flow by multi-angle scatter measurement and a support-vector machine classifier", Proc. SPIE 6441, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues V, 64410O (19 February 2007); https://doi.org/10.1117/12.699227

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available