title: Automatic detection of malaria parasite in blood images using two parameters.
abstract: Malaria must be diagnosed quickly and accurately at the initial infection stage and treated early to cure it properly. The malaria diagnosis method using a microscope requires much labor and time of a skilled expert and the diagnosis results vary greatly between individual diagnosticians. Therefore, to be able to measure the malaria parasite infection quickly and accurately, studies have been conducted for automated classification techniques using various parameters. In this study, by measuring classification technique performance according to changes of two parameters, the parameter values were determined that best distinguish normal from plasmodium-infected red blood cells. To reduce the stain deviation of the acquired images, a principal component analysis (PCA) grayscale conversion method was used, and as parameters, we used a malaria infected area and a threshold value used in binarization. The parameter values with the best classification performance were determined by selecting the value (72) corresponding to the lowest error rate on the basis of cell threshold value 128 for the malaria threshold value for detecting plasmodium-infected red blood cells.
authors.affiliation: Department of Convergence Software, Hallym University, Korea.|Bio-IT Research Center, Hallym University, Korea.
authors.forename: Jong-Dae
authors.initials: JD
authors.lastname: Kim
authors.affiliation: Department of Convergence Software, Hallym University, Korea.|Bio-IT Research Center, Hallym University, Korea.
authors.forename: Kyeong-Min
authors.initials: KM
authors.lastname: Nam
authors.affiliation: Department of Convergence Software, Hallym University, Korea.|Bio-IT Research Center, Hallym University, Korea.
authors.forename: Chan-Young
authors.initials: CY
authors.lastname: Park
authors.affiliation: Department of Convergence Software, Hallym University, Korea.|Bio-IT Research Center, Hallym University, Korea.
authors.forename: Yu-Seop
authors.initials: YS
authors.lastname: Kim
authors.affiliation: Department of Convergence Software, Hallym University, Korea.|Bio-IT Research Center, Hallym University, Korea.
authors.forename: Hye-Jeong
authors.initials: HJ
authors.lastname: Song
mesh_terms: D000818:Animals; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008288:Malaria / Q000097:blood / Q000175:diagnosis* / Q000382:microbiology*; D010961:Plasmodium / Q000302:isolation & purification*
keywords: Malaria; PCA; error rate; image processing; infected area; threshold