Automated Fecal Parasite Detection System

In this study, we propose a technique based on Filtration and Steady Determinations Thresholds System (F-SDTS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and other morphological process are applied for feature extraction sta...

Full description

Bibliographic Details
Main Authors:	Al-Sameraai, Raafat Salih Hadi, Zulkeflee, Kalidin, Kamarul Hawari, Ghazali, Zeehaida, Mohamed
Format:	Article
Language:	English
Published:	2013
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/3712/ http://umpir.ump.edu.my/id/eprint/3712/1/Automated_Fecal_Parasite_Detection_System-rafaat_fkee_journal.pdf

id	ump-3712
recordtype	eprints
spelling	ump-37122017-11-01T01:24:47Z http://umpir.ump.edu.my/id/eprint/3712/ Automated Fecal Parasite Detection System Al-Sameraai, Raafat Salih Hadi Zulkeflee, Kalidin Kamarul Hawari, Ghazali Zeehaida, Mohamed TK Electrical engineering. Electronics Nuclear engineering In this study, we propose a technique based on Filtration and Steady Determinations Thresholds System (F-SDTS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and other morphological process are applied for feature extraction stage of F-SDTS approach used in this study. The technique given in this study enables 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. In the first stage, a preprocessing subsystem is realized for obtaining unique features after performing noise reduction, contrast enhancement, segmentation. In the second stage, a feature extraction mechanism which is based on five features of the three characteristics (shape, shell smoothness, and size) is used. In the third stage, Filtration with Steady Determinations Thresholds System (F-SDTS) classifier is used for recognition process using the ranges of feature values as a database to identify and classify the type of parasite. We conducted computer simulations on MATLAB environment with a GUI as a friendly user. The overall success rates are almost 93% and 94% in Ascaris lumbricoides and Trichuris trichiura, respectively. 2013 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3712/1/Automated_Fecal_Parasite_Detection_System-rafaat_fkee_journal.pdf Al-Sameraai, Raafat Salih Hadi and Zulkeflee, Kalidin and Kamarul Hawari, Ghazali and Zeehaida, Mohamed (2013) Automated Fecal Parasite Detection System. American Journal of Scientific Research, Issue . pp. 87-96. ISSN 2301-2005 (1450-223x)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Al-Sameraai, Raafat Salih Hadi Zulkeflee, Kalidin Kamarul Hawari, Ghazali Zeehaida, Mohamed Automated Fecal Parasite Detection System
description	In this study, we propose a technique based on Filtration and Steady Determinations Thresholds System (F-SDTS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and other morphological process are applied for feature extraction stage of F-SDTS approach used in this study. The technique given in this study enables 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. In the first stage, a preprocessing subsystem is realized for obtaining unique features after performing noise reduction, contrast enhancement, segmentation. In the second stage, a feature extraction mechanism which is based on five features of the three characteristics (shape, shell smoothness, and size) is used. In the third stage, Filtration with Steady Determinations Thresholds System (F-SDTS) classifier is used for recognition process using the ranges of feature values as a database to identify and classify the type of parasite. We conducted computer simulations on MATLAB environment with a GUI as a friendly user. The overall success rates are almost 93% and 94% in Ascaris lumbricoides and Trichuris trichiura, respectively.
format	Article
author	Al-Sameraai, Raafat Salih Hadi Zulkeflee, Kalidin Kamarul Hawari, Ghazali Zeehaida, Mohamed
author_facet	Al-Sameraai, Raafat Salih Hadi Zulkeflee, Kalidin Kamarul Hawari, Ghazali Zeehaida, Mohamed
author_sort	Al-Sameraai, Raafat Salih Hadi
title	Automated Fecal Parasite Detection System
title_short	Automated Fecal Parasite Detection System
title_full	Automated Fecal Parasite Detection System
title_fullStr	Automated Fecal Parasite Detection System
title_full_unstemmed	Automated Fecal Parasite Detection System
title_sort	automated fecal parasite detection system
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/3712/ http://umpir.ump.edu.my/id/eprint/3712/1/Automated_Fecal_Parasite_Detection_System-rafaat_fkee_journal.pdf
first_indexed	2023-09-18T21:58:10Z
last_indexed	2023-09-18T21:58:10Z
_version_	1777414218835820544

Automated Fecal Parasite Detection System

Similar Items