Author(s): HIREMATH P.S., PARASHURAM BANNIGIDAD*, MANJUNATH HIREMATH
Journal: International Journal of Machine Intelligence
ISSN 0975-2927
Volume: 3;
Issue: 4;
Start page: 180;
Date: 2011;
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Keywords: Rotavirus | Adenovirus | Image segmentation | Image analysis | Active contour multigrid model | 3classifier | K-NN classifier | Neural network classifier
ABSTRACT
Accurate and reliable segmentation is an essential step in determining valuable quantitative information on size, shape and texture, which may assist microbiologists in their diagnoses. The snakes or active contours are used extensively in computer vision and image processing applications, particularly to locate object boundaries. The objective of the present study is to develop an automatic tool to identify and classify the virus particles in digital microscopic images using multigrid active contour model. Geometric features are used to identify the different types of virus particles, namely, Rotavirus and Adenovirus using 3classifier, K-NN classifier 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 virus particle classification by segmenting digital microscopic virus images and extracting geometric features for virus particle classification. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method.
Journal: International Journal of Machine Intelligence
ISSN 0975-2927
Volume: 3;
Issue: 4;
Start page: 180;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Rotavirus | Adenovirus | Image segmentation | Image analysis | Active contour multigrid model | 3classifier | K-NN classifier | Neural network classifier
ABSTRACT
Accurate and reliable segmentation is an essential step in determining valuable quantitative information on size, shape and texture, which may assist microbiologists in their diagnoses. The snakes or active contours are used extensively in computer vision and image processing applications, particularly to locate object boundaries. The objective of the present study is to develop an automatic tool to identify and classify the virus particles in digital microscopic images using multigrid active contour model. Geometric features are used to identify the different types of virus particles, namely, Rotavirus and Adenovirus using 3classifier, K-NN classifier 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 virus particle classification by segmenting digital microscopic virus images and extracting geometric features for virus particle classification. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method.
