Open AccessPattern recognition cytometry for label-free cell classification by 2D light scattering measurements
Author(s) -
Xuantao Su,
Shanshan Liu,
Qiao Xu,
Yan Yang,
Kun Song,
Beihua Kong
Publication year - 2015
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.027558
Subject(s) - light scattering , cytometry , adaboost , artificial intelligence , boosting (machine learning) , pattern recognition (psychology) , optics , computer science , scattering , physics , flow cytometry , support vector machine , biology , microbiology and biotechnology
We develop a pattern recognition cytometric technique for label-free cell classification. Two dimensional (2D) light scattering patterns from single cells and cell aggregates are obtained with a static cytometer. Good performance of the cytometric setup is verified by comparing yeast cell experimental results with theoretical simulations. Adaptive boosting (AdaBoost) method (a machine learning algorithm) is adopted for the analysis of the 2D light scattering patterns. It is shown that aggregates of three yeast cells can be well differentiated from aggregates of four yeast cells by this pattern recognition cytometric technique. We demonstrate that the pattern recognition cytometry can perform label-free classification of normal cervical cells and HeLa cells with a high accuracy rate.