In our previous study we have shown that identification of bacteria species with the use of Fresnel diffraction patterns is possible with high accuracy and at low cost. Fresnel diffraction patterns were recorded with the optical system with converging spherical wave illumination. Obtained experimental results have shown that colonies of specific bacteria species generate unique diffraction signatures. Features used for building classification models and thus for identification were simply mean value and standard deviation calculated of pixel intensities within regions of interest called rings. This work presents new, interpretable features denoting morphological and textural properties of the Fresnel diffraction patterns and their verification with the use of the statistical analysis workflow specially developed for bacteria species identification. As data set of bacteria species diffraction patterns it is very important to find features that differentiate species in the best manner. This task includes two steps. The first is finding and extracting new, interpretable features that can potentially be better for bacteria species differentiation than the ones used before. While the second one is deciding which of them are the best for identification purposes. The new features are calculated basing on normalized diffraction patterns and central statistical moments. For the verification the analysis workflow based on ANOVA for feature selection, LDA, QDA and SVM models for classification and identification and CV, sensitivity and specificity for performance assessment of the identification process, are applied. Additionally, the Fisher divergence method also known as signal to noise ratio (SNR) for feature selection was exploited.

Date Published: 24 May 2013
PDF: 7 pages
Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87911M (24 May 2013); doi: 10.1117/12.2020337

Published in SPIE Proceedings Vol. 8791:
Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection
Fabio Remondino; Jürgen Beyerer; Fernando Puente León; Mark R. Shortis, Editor(s)