000			01563nam a2200241 a 4500
001			vtls000077106
003			KUKTEM
005			20251114204602.0
008			140327t2013 my a f m 000 0 eng d
020			_aTHE0001858(Local)
039		9	_a201905131447 _byusri _y201403271131 _zFida
040			_aUMP
090			_aQA76.76.D47 S29 2013 rs Bc.
100	1		_aSaw, Hui Ann
245	1	0	_aAnalysis of microscopic blood samples for detecting malaria / _cSaw Hui Ann
260			_aKuantan, Pahang : _bUMP, _c2013
300			_ax, 64 p. : _bill. (some col.) ; _c30 cm.
502			_aProject paper (Bachelor of Computer Science (Graphic & Multimedia Technology) -- Universiti Malaysia Pahang - 2013
504			_aBibliography : p.42-44
520	3		_aMalaria 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 .
650		0	_aMicroscopy _xTechnique
999			_aVIRTUA40 _c4578 _d4584
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*9992