Detection of Escherichia Coli Bacteria in Water Using Deep Learning: A Faster R-CNN Approach

Considering its importance for vital activities, water and particularly drinking water should be clean and should not contain disease-causing bacteria. One of the pathogenic bacteria found in water is the bacterium Escherichia coli (E. coli). In the commonly used method for the detection of E. coli bacteria, the bacteria samples distilled from the water sample are seeded in endo agar medium and the change in the color of the medium as a result of the metabolic activities of the bacteria is examined with the naked eye. This color change can be seen with the human eye in approximately 22 ± 2 hours. In this study, a new bacteria detection scheme is proposed – using deep learning to detect E. coli bacteria both in shorter time and in practical way. The proposed technique is tested with experimentally collected data. Results show that the detection of bacteria can be done automatically within 6-10 hours with the proposed method

Methods of Classification of the Genera and Species of Bacteria Using Decision Tree, Journal of Telecommunications and Information Technology, 2019, nr 4

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 characteristic

Introdução à Análise de Movimento usando Visão Computacional

Pretende-se com este trabalho fazer uma introdução ao que tem vindo a ser realizado no domínio do seguimento e análise de movimento recorrendo a visão computacional.Assim no primeiro capítulo deste relatório faremos referência aos vários tipos de movimento e analisaremos as fases que compõem um sistema comum de captura e análise de movimento, descrevendo sucintamente alguns trabalhos realizados nesta área.Seguidamente, no segundo capítulo, faremos uma apresentação mais detalhada da área do seguimento e análise de movimento humano de corpo inteiro; nomeadamente, no reconhecimento da pose e do reconhecimento do andar e de gestos.Finalmente, no terceiro e último capítulo, daremos ênfase à análise de imagem médica e exemplificaremos, sumariamente, algumas das suas aplicações.With this work we intend to introduce what has been done in the domain of tracking and motion analysis by using computational vision.Therefore in the first chapter of this report we will refer the various types of motion, and analyse the steps that compose a general system of movement capture and analysis, by succinctly describing some works done in this field.Then, in the second chapter we will do a more detailed study about the area of human entire body tracking and motion analysis; namely, in pose recognition and in the recognition of gait and gestures.Finally, in the third and last chapter, emphasis will be given to the medical images analysis and we will summarily exemplify some of its applications

Image segmentation and object classification for automatic detection of tuberculosis in sputum smears

Includes bibliographical references (leaves 95-101).An automated microscope is being developed in the MRC/UCT Medical Imaging Research Unit at the University of Cape Town in an effort to ease the workload of laboratory technicians screening sputum smears for tuberculosis (TB), in order to improve screening in countries with a heavy burden of TB. As a step in the development of such a microscope, the project described here was concerned with the extraction and identification of TB bacilli in digital images of sputum smears obtained with a microscope. The investigations were carried out on Ziehl-Neelsen (ZN) stained sputum smears. Different image segmentation methods were compared and object classification was implemented using various two-class classifiers, for images obtained using a microscope with 100x objective lens magnification. The bacillus identification route established for the 100x images, was applied to images obtained using a microscope with 20x objective lens magnification. In addition, one-class classification was applied the 100x images. A combination of pixel classifiers performed best in image segmentation to extract objects of interest. For 100x images, the product of the Bayes’, quadratic and logistic linear classifiers resulted in a percentage of correctly classified bacillus pixels of 89.38%; 39.52% of pixels were incorrectly classified. The segmentation method did not miss any bacillus objects with their length in the focal plane of an image. The biggest source of error for the segmentation method was staining inconsistencies. The pixel segmentation method performed poorly on images with 20x magnification. Geometric change invariant features were extracted to describe segmented objects; Fourier coefficients, moment invariant features and colour features were used. All two-class object classifiers had balanced performance for 100x images, with sensitivity and specificity above 95% for the detection of an individual bacillus after Fisher mapping of the feature set. Object classification on images with 20x magnification performed similarly. One-class object classification using the mixture of Gaussians classifier, without Fisher mapping of features, produced sensitivity and specificity above 90% when applied to 100x images

Automatic Identification of Bacterial Types Using Statistical Imaging Methods

Abstract—The objective of the current study is to develop an automatic tool to identify microbiological data types using computervision and statistical modeling techniques. Bacteriophage (phage) typing methods are used to identify and extract representative profiles of bacterial types out of species such as the Staphylococcus aureus. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes. Index Terms—Bacteria image analysis, phage typing, spot finding, statistical modeling, visual-array data. I

Automatic Identification of Bacterial Types using Statistical Imaging Methods

The objective of the current study is to develop an automatic tool to identify bacterial types using computer-vision and statistical modeling techniques. Bacteriophage (phage)-typing methods are used to identify and extract representative profiles of bacterial types, such as the Staphylococcus Aureus. Current systems rely on the subjective reading of plaque profiles by human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes