Human parasitic worm detection using image processing technique

Intestinal parasites of protozoa and helminthes may cause disease or even death to animals and humans. In a current study of fecal sample examination to detect parasites, a technologist examines images manually using a lighted microscope. This method of examination is known to be inefficient when it...

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Main Authors: R. S., Hadi, Z., Khalidin, Kamarul Hawari, Ghazali, M., Zeehaida
Format: Conference or Workshop Item
Language:English
Published: IEEE 2012
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Online Access:http://umpir.ump.edu.my/id/eprint/26968/1/Human%20parasitic%20worm%20detection%20using%20image%20processing%20technique.pdf
http://umpir.ump.edu.my/id/eprint/26968/
https://doi.org/10.1109/ISCAIE.2012.6482095
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Institution: Universiti Malaysia Pahang
Language: English
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spelling my.ump.umpir.269682020-03-20T03:00:49Z http://umpir.ump.edu.my/id/eprint/26968/ Human parasitic worm detection using image processing technique R. S., Hadi Z., Khalidin Kamarul Hawari, Ghazali M., Zeehaida TK Electrical engineering. Electronics Nuclear engineering Intestinal parasites of protozoa and helminthes may cause disease or even death to animals and humans. In a current study of fecal sample examination to detect parasites, a technologist examines images manually using a lighted microscope. This method of examination is known to be inefficient when it involves a large number of samples. On top of that, it is very important to introduce a system that is capable of assisting the technologist in the examination of fecal samples. In this paper, an automatic process is proposed to detect different types of parasites from fecal samples using an image processing technique. Image processing techniques have been introduced to automatically screen the existence of parasites in human fecal specimens. This process involves methods such as noise reduction, contrast enhancement, segmentation, and morphological analysis. At the classification stage, we propose a simple classification method using logical threshold, whereby the ranges of feature values have been identified to classify the type of parasite. The proposed system has been tested with 100 parasite images of each class, which promotes accuracy. IEEE 2012 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26968/1/Human%20parasitic%20worm%20detection%20using%20image%20processing%20technique.pdf R. S., Hadi and Z., Khalidin and Kamarul Hawari, Ghazali and M., Zeehaida (2012) Human parasitic worm detection using image processing technique. In: IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE 2012), 3-4 December 2012 , Kota Kinabalu, Sabah. pp. 196-201.. ISBN 978-1-4673-3033-6 https://doi.org/10.1109/ISCAIE.2012.6482095
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
R. S., Hadi
Z., Khalidin
Kamarul Hawari, Ghazali
M., Zeehaida
Human parasitic worm detection using image processing technique
description Intestinal parasites of protozoa and helminthes may cause disease or even death to animals and humans. In a current study of fecal sample examination to detect parasites, a technologist examines images manually using a lighted microscope. This method of examination is known to be inefficient when it involves a large number of samples. On top of that, it is very important to introduce a system that is capable of assisting the technologist in the examination of fecal samples. In this paper, an automatic process is proposed to detect different types of parasites from fecal samples using an image processing technique. Image processing techniques have been introduced to automatically screen the existence of parasites in human fecal specimens. This process involves methods such as noise reduction, contrast enhancement, segmentation, and morphological analysis. At the classification stage, we propose a simple classification method using logical threshold, whereby the ranges of feature values have been identified to classify the type of parasite. The proposed system has been tested with 100 parasite images of each class, which promotes accuracy.
format Conference or Workshop Item
author R. S., Hadi
Z., Khalidin
Kamarul Hawari, Ghazali
M., Zeehaida
author_facet R. S., Hadi
Z., Khalidin
Kamarul Hawari, Ghazali
M., Zeehaida
author_sort R. S., Hadi
title Human parasitic worm detection using image processing technique
title_short Human parasitic worm detection using image processing technique
title_full Human parasitic worm detection using image processing technique
title_fullStr Human parasitic worm detection using image processing technique
title_full_unstemmed Human parasitic worm detection using image processing technique
title_sort human parasitic worm detection using image processing technique
publisher IEEE
publishDate 2012
url http://umpir.ump.edu.my/id/eprint/26968/1/Human%20parasitic%20worm%20detection%20using%20image%20processing%20technique.pdf
http://umpir.ump.edu.my/id/eprint/26968/
https://doi.org/10.1109/ISCAIE.2012.6482095
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