Bayesian 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, Noor Amaleena, Jusoh Awang, Noorain, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win
Format:	Article
Language:	English
Published:	AIRCC Publishing Corporation 2014
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/38132/ http://irep.iium.edu.my/38132/ http://irep.iium.edu.my/38132/1/1114caij01.pdf

id	iium-38132
recordtype	eprints
spelling	iium-381322018-06-19T04:14:28Z http://irep.iium.edu.my/38132/ Bayesian prokaryote classification from microscopic images Mohamad, Noor Amaleena Jusoh Awang, Noorain 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. AIRCC Publishing Corporation 2014-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/38132/1/1114caij01.pdf Mohamad, Noor Amaleena and Jusoh Awang, Noorain and Htike@Muhammad Yusof, Zaw Zaw and Shoon , Lei Win (2014) Bayesian prokaryote classification from microscopic images. Computer Applications: An International Journal (CAIJ), 1 (1). pp. 1-9. ISSN 2393-8455 http://airccse.com/caij/current.html
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, Noor Amaleena Jusoh Awang, Noorain Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win Bayesian 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, Noor Amaleena Jusoh Awang, Noorain Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win
author_facet	Mohamad, Noor Amaleena Jusoh Awang, Noorain Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win
author_sort	Mohamad, Noor Amaleena
title	Bayesian prokaryote classification from microscopic images
title_short	Bayesian prokaryote classification from microscopic images
title_full	Bayesian prokaryote classification from microscopic images
title_fullStr	Bayesian prokaryote classification from microscopic images
title_full_unstemmed	Bayesian prokaryote classification from microscopic images
title_sort	bayesian prokaryote classification from microscopic images
publisher	AIRCC Publishing Corporation
publishDate	2014
url	http://irep.iium.edu.my/38132/ http://irep.iium.edu.my/38132/ http://irep.iium.edu.my/38132/1/1114caij01.pdf
first_indexed	2023-09-18T20:54:44Z
last_indexed	2023-09-18T20:54:44Z
_version_	1777410228019527680

Bayesian prokaryote classification from microscopic images

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