Methods of Classification of the Genera and Species of Bacteria Using Decision Tree

Author:

Plichta AnnaORCID

Abstract

This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristics.

Publisher

National Institute of Telecommunications

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Reference50 articles.

1. [1] P. Lutomski et al., "Wykorzystanie informatyki medycznej w Polsce i na świecie" (An Application of the medical computer sciences in Poland and worldwide), Przedsiębiorczość i Zarządzanie, vol. 3, no. 15, pp. 83-92, 2014 [Online]. Available: http://piz.san.edu.pl/docs/e-XV-12-3.pdf [in Polish].

2. [2] R. Rudowski, Informatyka Medyczna. Wydawnictwo Naukowe PWN, 2003 (ISBN: 9788301140564) [in Polish].

3. [3] R. Tadeusiewicz and W. Wajs, Informatyka Medyczna. Uczelniane Wyd. Naukowo-Dydaktyczne Akademii Górniczo-Hutniczej, 1999 [in Polish].

4. [4] F. Sahba and H. R. Tizhoosh, "Filter fusion for image enhancement using reinforcement learning", in Proc. Canadian Conf. on Elec. and Comp. Engin. CCECE 2003, Montreal, Quebec, Kanada, 2003, vol. 2, pp. 847-850 (doi: 10.1109/CCECE.2003.1226027).

5. [5] M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision. Cengage Learning, 2014 (ISBN: 9781133593690).

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