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Analysis of microscopic blood samples for detecting malaria / Saw Hui Ann

By:

Saw, Hui Ann

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2013Description: x, 64 p. : ill. (some col.) ; 30 cmISBN:

THE0001858(Local)

Subject(s):

Microscopy -- Technique

Dissertation note: Project paper (Bachelor of Computer Science (Graphic & Multimedia Technology) -- Universiti Malaysia Pahang - 2013 Abstract: Malaria is a mosquito-borne disease and it has been affecting millions of people worldwide since decades ago. The conventional method in diagnosing the blood disease is by using manual visual examination of microscopy blood smears. However, a computer-assisted system can be designed to assist in malaria diagnosis by employing image processing, analysis and feature recognition algorithm. In terms of enhancing image for analysis, this study explores on a new approach by averaging results of two filters. In order to evaluate the performance of the proposed method, the image was further clustered by using K-Means, Expectation Maximization (EM) and Otsu’s threshold algorithm .

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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	CD 7656 \| QA76.76.D47 S29 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078956
Final Year Report	UMPLIB PEKAN	QA76.76.D47 S29 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078955

Project paper (Bachelor of Computer Science (Graphic & Multimedia Technology) -- Universiti Malaysia Pahang - 2013

Bibliography : p.42-44

Malaria is a mosquito-borne disease and it has been affecting millions of people worldwide since decades ago. The conventional method in diagnosing the blood disease is by using manual visual examination of microscopy blood smears. However, a computer-assisted system can be designed to assist in malaria diagnosis by employing image processing, analysis and feature recognition algorithm. In terms of enhancing image for analysis, this study explores on a new approach by averaging results of two filters. In order to evaluate the performance of the proposed method, the image was further clustered by using K-Means, Expectation Maximization (EM) and Otsu’s threshold algorithm .