Optimization of General Threshold Value for Preprocessing in Plasmodium Parasites Detection

Abstract

The high mortality rate of malaria makes it a severe disease that spreads throughout all-region by infected female Anopheles mosquitoes, especially in tropical countries. Accurate early malaria detection is one of the ways to reduce the mortality rate. Microscopy-based malaria examinations are still considered the gold standard. Due to numerous large malaria patients with limited parasitologists, an automated detection system is needed as a second opinion to assist parasitologists. This study proposed an optimization method for finding an optimal global threshold value for pre-processing parasite detection. There were three stages of the proposed method. The first is to pre-process digital microscopic images using color channel selection, contrast stretching, and morphological operation. The second is to find the global threshold value using multiple modified Otsu’s. The third is to determine the optimum global threshold value. In the last stage, predicted threshold values are generated using a pattern recognition approach to determine the optimum global threshold value. The proposed method evaluated 468 microscopic images captured from hundreds of thin smear blood slides. The slides are provided by the Department of Parasitology-UGM and the Eijkman Institute for Molecular Biology. The set image contains 691 malaria parasites in all types and life stages of malaria parasites. The proposed method obtained a sensitivity of 99.6 % and the smallest FPs number compared to without the optimization. It indicates that the proposed method has the potential to be implemented in the initial stages of the malaria detection system.