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Automated Fecal Parasite Detection System

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)

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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.

Item Type: Article
Additional Information: Mr. Raafat Salih Hadi Al-Sameraai MEE11001 FKEE
Uncontrolled Keywords: Automatic parasitic detection; Image processing; MATLAB toolbox
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 14 May 2013 07:21
Last Modified: 01 Nov 2017 01:24
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