Automated image processing method for the diagnosis and classification of malaria on thin blood smears
Automated image processing method for the diagnosis and classification of malaria on thin blood smears
复制标题
DOI：
10.1007/s11517-006-0044-2
复制
发表时间：
2006-05-01
影响因子：
3.2
通讯作者：
Duse, Adriano G.
中科院分区：
文献类型：
--
作者：
Ross, Nicholas E.;Pritchard, Charles J.;Duse, Adriano G.
疟疾是一个严重的全球健康问题，需要快速、准确的诊断来控制这种疾病。开发了一种图像处理算法，用于自动诊断疟疾的稀薄血液涂片。图像分类系统旨在肯定地识别存在于稀薄血液涂片中的疟疾寄生虫，并区分疟疾的种类。图像是使用连接到光学显微镜的电荷耦合设备相机来获取的。形态和新颖的阈值选择技术被用来识别存在于显微镜载玻片上的红细胞(红细胞)和可能的寄生虫。生成基于颜色、纹理和细胞和寄生虫几何形状的图像特征，以及利用分类问题的先验知识和人类技术人员使用的模仿特征的特征。使用反向传播前向神经网络的两级树分类器区分真阳性和假阳性，然后诊断感染的种类(恶性疟原虫、间日疟原虫、卵形疟原虫或疟疾)。从威特沃特斯兰德大学医学院临床微生物学和传染病系获得的疟疾样本用于该系统的培训和测试。感染红细胞的阳性鉴定灵敏度为85%，阳性预测值(PPV)为81%，这使得该方法在诊断完整样本时具有很高的敏感性，只要分析了许多观点。在15个样品中，有11个样品的物种被正确确定。
Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are 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 the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural 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 Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples.