Identification and classification of cocci bacterial cells in digital microscopic images

Abstract

In cytology, automating the feature extraction process yields an objective, quantitative, detailed and reproducible computation of cell morphofunctional characteristics and allows the analysis of a large quantity of images. The objective of the present study is to develop an automatic tool to identify and classify the different types of cocci bacterial cells in digital microscopic cell images. Geometric features are used to identify the arrangement of cocci bacterial cells, namely cocci, diplococci, streptococci, tetrad, sarcinae and staphylococci using 3σ, K-NN and Neural network classifiers. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for cocci bacterial cell classification by segmenting digital bacterial cell images and extracting geometric and statistical features for cell classification. The experimental results are compared with the manual results obtained by microbiology expert and other methods in the literature.

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

“…The proposed ANN achieved good identification accuracy of 94%. Hiremath et al [ 31 ] presented a ML based approach for the image classification of six types of bacterial cells namely, cocci , streptococci , diplococci , staphylococcus , tetrad and sarcinae . The approach worked by acquiring 350 bacterial cell images consisted of bacterial cells under study.…”

Digital Algal Cell Growth Analysis and Time Determination of Pediastrum sp using Fuzzy Inference System

“…[4]. Automated identification and classification of bacilli bacterial cell growth phases based on geometric features by Hiremath and Parashuram [5] The characteristics that need to be considered and studied in the present work are the size of the cells before and after division, the variation in the generation time of algal cells, spatial and successive change in morphology of the individual cell size. The factors that play a vital role in changes in morphological growth phases during the life cycle of microalgae are temperature, light, pH, and medium components.…”

Spiral Bacterial Cell Image Analysis using Active Contour Method

“…The data mining techniques are employed for the classification of HEp-2 cells by Petra Perner [11], in which a simple set of shape features are used for classification of bacterial cells. Hiremath and Parashuram [4,9] have investigated the automatic classification of cocci and spiral bacterial cells and its sub groups using digital microscopic images using geometric shape features. A computer-aided system for the image analysis of bacterial morphotypes in microbial communities using geometric shape features has been investigated by J. Liu et al [7].…”

“…Although the comparison of classification performance of the various state-of-the art methods in the literature is difficult because of the different cell image data sets used for experimentation, it may be observed that, in [1] statistical modeling techniques are applied for staphylococcus aureus cells and has yielded 98%, in [3] data mining approach was used for HEp-2 cells and has yielded 86.67% classification rate, in [2] neural network approach has yielded above 90% classification rate in the various different types of bacterial cells and in [4] the statistical methodology has yielded classification rates in the range 89% to 98% for different categorization methods for fluorescent labeled cells. In [15] statistical analysis method for classification of various bacterioplankton groups was used and has yielded 80% overall accuracy.…”