Content Based Image Retrieval Approaches forDetection of Malarial in Blood Images

International Journal of Biometric and Bioinformatics

Jigyasa Soni¹  Bibhudendra Acharya¹  Bikesh Kumar Singh¹  Mohammad Imroze Khan¹ 

关键词: Falciparum;    Vivax;    Malariae;    Giemsa;   

DOI  :  

学科分类：计算机科学（综合）

来源: Computer Science Journals

【 摘 要 】

Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An imageprocessing algorithm to automate the diagnosis of malaria in blood images is proposed in this paper. The image classification system isdesigned to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images areacquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniquesare used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour,texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of theclassification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforwardneural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P.ovale or P. malariae) of the infection. Malaria samples obtained from the various biomedical research facilities are used for training andtesting of the system. Infected erythrocytes are positively identified with two measurable parameters namely sensitivity and a positivepredictive value (PPV), which makes the method highly sensitive at diagnosing a complete sample, provided many views are analyzed.

【 授权许可】

Unknown