Next-Generation Image Segmentation for Enhanced Malaria Detection and Diagnosis
Edy Victor Haryanto · Journal of Image and Graphics · 2025
Malaria, a life-threatening disease transmitted by Anopheles mosquitoes, continues to pose a significant global health challenge, particularly in Africa where the majority of cases and deaths occur.This study addresses the urgent need for improved diagnostic techniques to enhance the accuracy and efficiency of malaria detection.Blood smear images of Plasmodium falciparum and Plasmodium vivax were collected from Dr. Pirngadi Medan Hospital, Indonesia.The primary goal of this research is to enhance the quality of these blood smear images for better identification of Plasmodium parasites using advanced image segmentation techniques.The novelty of this study lies in the application of Adaptive Global Contrast Stretching (AGCS) to improve image contrast and reduce noise, followed by color transformation into the Hue, Saturation, Value (HSV) color space.The saturation component of the HSV space is then segmented using adaptive thresholding, and artifacts are removed through morphological processing and active contour methods.The results demonstrate that the AGCS technique significantly enhances image quality, making Plasmodium parasites more visible and facilitating more accurate and timely diagnoses.The implications of this research are profound, offering a scalable and robust solution for malaria diagnosis that can be integrated into automated systems, thereby reducing the reliance on skilled technicians and minimizing human error.This enhanced diagnostic approach is crucial for effective disease management and has the potential to significantly reduce malaria-related mortality rates.