Plasmodium Candidate Detection on Thin Blood Smear Images with Luminance Noise Reduction

Malaria is one of the deadliest diseases over the world. It leads to a serious vector-borne disease caused by a blood parasite of the Plasmodium genus. WHO declared that microscopy-based has become “the gold standard” in detecting malaria. However, manual procedure conducted by human experts may cause fatigue and leads to human error in assessment. Several computer aided detection (CAD) systems to assist parasitologists in detecting malaria have been published. However, the presence of illuminant noise is still a challenge for researchers. This paper proposes a new malaria detection scheme efficiently abled in microscopy image presented illumination noise. By using GGB normalization and gamma enhancement, the parasites are easier to detect. The proposed scheme achieves the best of sensitivity value compared the previous schemes. This result indicates that the proposed scheme in detecting parasite has a potential contribution in the development of CAD for Plasmodium detection.