Automated Image Analysis Method for p-vivax Malaria Parasite Detection in Thick Film Blood Images

Leonardo Yunda, Andrés Alarcón, Jorge Millán


An image analysis method for Malaria parasite detection in thick film blood images is described. The developed method uses a combination of AGNES and Morphological Gradient techniques in the image segmentation stage. Wavelet-based feature extraction is followed by a neural network classification stage. Principal Component Analysis (PCA) is used to reduce the number of features and improve the performance of the neuronal network.  The true positive rate for determining a specific parasite was of 77.19%, while a 76.45% was obtained in determining at least a parasite in a microscopy image.


Malaria; Thick film microscopy; Neuronal networks; principal component analysis.


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