Comparison of Image Improvement Method on Parasite Images of Malaria - Rika Rosnelly; Linda Wahyuni (Universitas Dian Nuswantoro and IndoCEISS, 2017)
Improved 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 contrast 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, malariae along with stage that is ring, tropozoit, skizon 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.