Classification of human parasitic worm using microscopic image processing technique

Human parasitic infection causes diseases to people whether this infection will be inside the body called endoparasites, or outside of the body called ectoparasites. Human intestinal parasite worms infected by air, food, and water are the causes of major diseases and health problems. So in this stud...

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Main Author:	Raafat Salih , Hadi
Format:	Thesis
Language:	English English English
Published:	2013
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/7597/ http://umpir.ump.edu.my/id/eprint/7597/ http://umpir.ump.edu.my/id/eprint/7597/1/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28Table%20of%20content%29.pdf http://umpir.ump.edu.my/id/eprint/7597/2/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28Abstract%29.pdf http://umpir.ump.edu.my/id/eprint/7597/3/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28References%29.pdf

id	ump-7597
recordtype	eprints
spelling	ump-75972017-04-03T06:00:49Z http://umpir.ump.edu.my/id/eprint/7597/ Classification of human parasitic worm using microscopic image processing technique Raafat Salih , Hadi TK Electrical engineering. Electronics Nuclear engineering Human parasitic infection causes diseases to people whether this infection will be inside the body called endoparasites, or outside of the body called ectoparasites. Human intestinal parasite worms infected by air, food, and water are the causes of major diseases and health problems. So in this study, a technique to identify two types of parasites in human fecal, that is, the eggs of the worms is proposed. In this strategy, digital image processing methods such as noise reduction, contrast enhancement, and other morphological process are applied to extract the eggs images based on their features. The technique suggested in this study enables us to classify two different parasite eggs from their microscopic images which are roundworms (Ascaris lumbricoides ova, ALO) and whipworms (Trichuris trichiura ova, TTO). This proposed recognition method includes three stages. The first stage is a pre-processing sub-system, which is used to obtain unique features after performing noise reduction, contrast enhancement, edge enhancement, and detection. The next stage is an extraction mechanism which is based on five features of the three characteristics (shape, shell smoothness, and size. The final stage, the Filtration with Determinations Thresholds System (F-DTS) classifier is used to recognize the process using the ranges of feature values as a database to identify and classify the two types of parasites. The overall success rates are 93% and 94% in Ascaris lumbricoides and Trichuris trichiura, respectively. 2013-07 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7597/1/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28Table%20of%20content%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/7597/2/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28Abstract%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/7597/3/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28References%29.pdf Raafat Salih , Hadi (2013) Classification of human parasitic worm using microscopic image processing technique. Masters thesis, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:78861&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Raafat Salih , Hadi Classification of human parasitic worm using microscopic image processing technique
description	Human parasitic infection causes diseases to people whether this infection will be inside the body called endoparasites, or outside of the body called ectoparasites. Human intestinal parasite worms infected by air, food, and water are the causes of major diseases and health problems. So in this study, a technique to identify two types of parasites in human fecal, that is, the eggs of the worms is proposed. In this strategy, digital image processing methods such as noise reduction, contrast enhancement, and other morphological process are applied to extract the eggs images based on their features. The technique suggested in this study enables us to classify two different parasite eggs from their microscopic images which are roundworms (Ascaris lumbricoides ova, ALO) and whipworms (Trichuris trichiura ova, TTO). This proposed recognition method includes three stages. The first stage is a pre-processing sub-system, which is used to obtain unique features after performing noise reduction, contrast enhancement, edge enhancement, and detection. The next stage is an extraction mechanism which is based on five features of the three characteristics (shape, shell smoothness, and size. The final stage, the Filtration with Determinations Thresholds System (F-DTS) classifier is used to recognize the process using the ranges of feature values as a database to identify and classify the two types of parasites. The overall success rates are 93% and 94% in Ascaris lumbricoides and Trichuris trichiura, respectively.
format	Thesis
author	Raafat Salih , Hadi
author_facet	Raafat Salih , Hadi
author_sort	Raafat Salih , Hadi
title	Classification of human parasitic worm using microscopic image processing technique
title_short	Classification of human parasitic worm using microscopic image processing technique
title_full	Classification of human parasitic worm using microscopic image processing technique
title_fullStr	Classification of human parasitic worm using microscopic image processing technique
title_full_unstemmed	Classification of human parasitic worm using microscopic image processing technique
title_sort	classification of human parasitic worm using microscopic image processing technique
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/7597/ http://umpir.ump.edu.my/id/eprint/7597/ http://umpir.ump.edu.my/id/eprint/7597/1/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28Table%20of%20content%29.pdf http://umpir.ump.edu.my/id/eprint/7597/2/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28Abstract%29.pdf http://umpir.ump.edu.my/id/eprint/7597/3/Classification%20of%20human%20parasitic%20worm%20using%20microscopic%20image%20processing%20technique%20%28References%29.pdf
first_indexed	2023-09-18T22:04:21Z
last_indexed	2023-09-18T22:04:21Z
_version_	1777414608728883200

Classification of human parasitic worm using microscopic image processing technique

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