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005 20251117113348.0
008 170511s2016 my fq d 000 0 eng d
020 _aTHE0005151(Local)
039 9 _a201905131737
_bhanafiah
_c201712261058
_dnazri
_c201705161130
_dnazri
_y201705111023
_znazri
040 _aUMP
090 _aFKEE .S25 2016 r Bc.
090 _aCD 10730
100 0 _aSakinah Mazlan
245 1 0 _aStudy on possibility to determinejaundice symptom using vision system
_h[electronic resource] /
_cSakinah Mazlan
260 _aKuantan, Pahang :
_bUMP,
_c2016
300 _a1 computer disc :
_bdigital data ;
_c12 cm.
500 _aFaculty of Electrical & Electronics Engineering
502 _aProject Paper (Bachelor of Electrical Engineering (Hons.) (Electronics)) -- Universiti Malaysia Pahang – 2016
520 3 _aJaundice is one of the most common disease affecting neonates worldwide caused by hyperbilirubinaemia in blood, which results in the appearance of yellow discolouration forming on the skin & white eyes (sclera). In the rise in today’s technology, apparently one of the most popular methods used in our country used for detection and confirming the symptoms is by blood sampling and other clinical testing with special equipment. But the main issue here is the blood sampling as it seems to be a bit painful especially for infants who are mostly less than 30 days old. One of the non-invasive methods is by measuring the level of bilirubin in blood using a bilirubinometer. It is a small accurate device which uses spectrophotometry but this small device has a really steep price tag, around $500 - $2000 a unit. So, the aim of this study is creating an affordable non-invasive system to determine whether a person is jaundice patient by detecting the yellow component on the skin using vision system. Throughout the progress, around 19 pictures of various samples (13 jaundiced & 6 healthy) were taken and colour analysis (RGB) was done on each samples in a software (MatLab). From the colour spaces mentioned, the mean values of each component were calculated and used to plot a distribution graph. Then, the graphs were observed to differentiate any obvious trends happening between the healthy and jaundiced samples. From RGB graph, yellow component is extracted from Green - Blue component and all 13 jaundiced samples shows higher yellow component than the healthy samples. A graphical user interface (GUI) is used to give a clearer view and better understanding for the audience.
538 _aItem in pdf format
610 2 0 _aFaculty of Electrical & Electronics Engineering
_xDissertations
650 0 _aUniversities and Colleges
_xDissertations
650 0 _aTheses
856 4 0 _uhttp://ecollib.ump.edu.my/id/eprint/25802
_zAccess in library only
999 _aVIRTUA40
_c7365
_d7371
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