Detection of Malaria Parasite Based on Thick and Thin Blood Smear Images Using Local Binary Pattern Satishkumar L. Varma and Satishkumar S. Chavan Abstract Malaria is one of the dangerous diseases transmitted by a female Anophe- les 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 morpho- logical 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. Keywords Red blood cells · Blood smear · Segmentation · Morphological operation · Malaria Parasite · k-means clustering · Local Binary Pattern 1 Introduction Infections and spread of diseases due to mosquitoes are real challenges in rural as well as urban areas of the world. Malaria is the most common and dangerous disease caused by a female Anopheles mosquito with the help of parasites. It is a very infectious disease of humans and other animals. It remains one of the most widespread infectious diseases of mankind with 216 million cases worldwide in 91 S. L. Varma Pillai College of Engineering, Panvel, Navi Mumbai, India e-mail: varmasl@yahoo.co.in S. S. Chavan (B ) Don Bosco Institute of Technology, Kurla (W), Mumbai, India e-mail: satyachavan@yahoo.co.in © Springer Nature Singapore Pte Ltd. 2019 B. Iyer et al. (eds.), Computing, Communication and Signal Processing, Advances in Intelligent Systems and Computing 810, https://doi.org/10.1007/978-981-13-1513-8_98 967