Image Analysis Based System for Automatic Detection of Malarial Parasite in Blood Images

Malaria is an infectious disease caused by microorganism and it poses major threat to global health zone. Brisk and accurate diagnosis is required to control the disease. The proposed system mainly concentrate on development of sensitive malarial detection system for images of (JSB) stained thick blood slides acquired from conventional light microscopes. Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Light microscopy enables the visualization of malarial parasites in a thick or thin smear of the patients blood. Automation of the evaluation process in the diagnosis of malaria is of high importance. The proposed system describes the computerized method of image analysis involving three main phases pre-processing, where the images are corrected for luminance and transformed to a constant color space. A histogram based image segmentation processing where the maximum artefacts and over stained objects are avoided. Finally, Feature extraction along with a multi-layer, feedforward, backpropagation neural network was employed for classifying the objects as parasite/wbc. Support Vector Machine (SVM) models are a close cousin to classical multilayer perceptron neural networks. The objective of the project is to develop an image processing algorithm to automate the diagnosis of malaria on thin blood smears.