Detection of Malaria Parasite Based on Thick and Thin Blood Smear Images Using Local Binary Pattern

Abstract

Malaria is one of the dangerous diseases transmitted by a female Anopheles mosquito through parasites. Parasite is a type of microorganism. Microscopic examination of blood samples helps to diagnose malaria automatically and faster. It also reduces the time and human errors. This paper aims to experiment and analyze quickly the accurate number of malaria parasites using image processing techniques. Local binary pattern (LBP) technique is used to classify blood smear into thin and thick blood smears. Morphological operations and k-means clustering techniques along with intensity profiles within the cells are used to count infected cells. The experiments are performed over standard datasets using segmentation and morphological operations for thick and thin blood smear images. The performance of the proposed algorithm is evaluated using confusion matrix. The results are compared using sensitivity and specificity. This method proves to be much effective in terms of time considering large rural areas in India.