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...
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| Format: | Article |
| Language: | en |
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2013
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| Online Access: | http://umpir.ump.edu.my/id/eprint/3712/1/Automated_Fecal_Parasite_Detection_System-rafaat_fkee_journal.pdf http://umpir.ump.edu.my/id/eprint/3712/ |
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| 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 |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.3712 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2013 |
| record_format | eprints |
| spelling | my.ump.umpir.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). (Published) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Al-Sameraai, Raafat Salih Hadi Zulkeflee, Kalidin Kamarul Hawari, Ghazali Zeehaida, Mohamed Automated Fecal Parasite Detection System |
| title | 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_short | Automated Fecal Parasite Detection System |
| title_sort | automated fecal parasite detection system |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/3712/1/Automated_Fecal_Parasite_Detection_System-rafaat_fkee_journal.pdf http://umpir.ump.edu.my/id/eprint/3712/ |
| url_provider | http://umpir.ump.edu.my/ |