Please use this identifier to cite or link to this item: http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3303
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dc.contributor.authorRosnelly, Rika-
dc.date.accessioned2019-05-10T03:10:32Z-
dc.date.available2019-05-10T03:10:32Z-
dc.date.issued2016-10-05-
dc.identifier.urihttp://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3303-
dc.descriptionpeer reviewen_US
dc.description.abstractImproved image is a process on the image that initially has a quality that is less good or has noise. In this image improvement operation image quality will be improved so that the image produces better quality. Image improvement methods used are contrasted stretching, histogram equalization, low pass filter and Gaussian filtering. In this study compare contrast stretching method, histogram equalization, low pass filter and Gaussian filtering to improve image quality. Performance of each method would be calculated by finding the value of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). This study compares contrast stretching methods, histogram equalization, low pass filter and Gaussian filtering to improve image quality. Total data of malaria parasite image is 120. The data consist of image of malaria parasite falciparum, vivax, malaria along with stage that is ring, trophozoite, schizont and gametocyte. Evaluate the performance of each method by finding Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) values. The result is a contrast stretching provides better image quality against malaria parasite image.en_US
dc.subjectcontrast stretchingen_US
dc.subjecthistogram equalizationen_US
dc.subjectGaussian filteringen_US
dc.subjectMSEen_US
dc.titleComparison of Image Improvement Method on Parasite Images of Malariaen_US
dc.typeOtheren_US
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