Digital image analysis of cocci bacterial cells using active contour method

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 using active contour method. Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Geometric features are used to identify the arrangement of cocci bacterial cells, namely, cocci, diplococci, streptococci, tetrad, sarcinae and staphylococci using 3s, 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 features for cell classification. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method. The experimentation is done using SEM digital images of various cocci bacterial communities.