Advances in prokaryote classification from microscopic images

Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification fr...

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Main Authors:	Mohamad , Amaleena, Jusoh, Noorain A., Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win
Format:	Article
Language:	English
Published:	Wireilla Scientific Publications, Australia 2014
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/38594/ http://irep.iium.edu.my/38594/ http://irep.iium.edu.my/38594/ http://irep.iium.edu.my/38594/1/3214ecij02.pdf

id	iium-38594
recordtype	eprints
spelling	iium-385942018-06-12T04:42:49Z http://irep.iium.edu.my/38594/ Advances in prokaryote classification from microscopic images Mohamad , Amaleena Jusoh, Noorain A. Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win Q Science (General) Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework comprises two steps. In the first step, the system is trained using a set of microscopic images containing Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features. Edge-based descriptors are then extracted from the input images. In the second step, we use the Naïve Bayes classifier to perform probabilistic inference based on the input descriptors. 64 images for each class of bacteria were used as the training set and 222 images consisting of the three classes of bacteria and other random images such as humans and airplanes were used as the test set. There are no images overlapped between the training set and the test set. The system was found to be able to accurately discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step automatic bacteria identification approach and motivate us to extend this framework to identify a variety of other types of bacteria. Wireilla Scientific Publications, Australia 2014-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/38594/1/3214ecij02.pdf Mohamad , Amaleena and Jusoh, Noorain A. and Htike@Muhammad Yusof, Zaw Zaw and Shoon , Lei Win (2014) Advances in prokaryote classification from microscopic images. Electrical & Computer Engineering: An International Journal (ECIJ), 3 (2). pp. 13-24. ISSN 2201-5957 http://wireilla.com/engg/ecij/current2014.html 10.14810/ecij.2014.3202
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Mohamad , Amaleena Jusoh, Noorain A. Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win Advances in prokaryote classification from microscopic images
description	Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework comprises two steps. In the first step, the system is trained using a set of microscopic images containing Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features. Edge-based descriptors are then extracted from the input images. In the second step, we use the Naïve Bayes classifier to perform probabilistic inference based on the input descriptors. 64 images for each class of bacteria were used as the training set and 222 images consisting of the three classes of bacteria and other random images such as humans and airplanes were used as the test set. There are no images overlapped between the training set and the test set. The system was found to be able to accurately discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step automatic bacteria identification approach and motivate us to extend this framework to identify a variety of other types of bacteria.
format	Article
author	Mohamad , Amaleena Jusoh, Noorain A. Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win
author_facet	Mohamad , Amaleena Jusoh, Noorain A. Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win
author_sort	Mohamad , Amaleena
title	Advances in prokaryote classification from microscopic images
title_short	Advances in prokaryote classification from microscopic images
title_full	Advances in prokaryote classification from microscopic images
title_fullStr	Advances in prokaryote classification from microscopic images
title_full_unstemmed	Advances in prokaryote classification from microscopic images
title_sort	advances in prokaryote classification from microscopic images
publisher	Wireilla Scientific Publications, Australia
publishDate	2014
url	http://irep.iium.edu.my/38594/ http://irep.iium.edu.my/38594/ http://irep.iium.edu.my/38594/ http://irep.iium.edu.my/38594/1/3214ecij02.pdf
first_indexed	2023-09-18T20:55:26Z
last_indexed	2023-09-18T20:55:26Z
_version_	1777410272679428096

Advances in prokaryote classification from microscopic images

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