■利什曼病是由利什曼原虫属的原生动物寄生虫引起的疾病,具有很高的患病率和对全球健康的影响。目前,可用的治疗药物有缺点,如高毒性,寄生虫的抗性,和高成本。因此,寻找新的,更有效,安全药物是当务之急。通过体外研究分析了抗利什曼虫药物的有效性,其中技术人员手动计数巨噬细胞内寄生虫(amastigote)的细胞内形式,这是缓慢的,辛苦,并且容易出错。
■开发一种计算系统,该系统有助于使用图像处理技术从体外研究获得的显微镜图像中对amastigotes进行检测和计数。
■使用多级Otsu方法对色调的饱和度分量对显微镜图像中可能是利什曼原虫的对象进行了分割,饱和度,和强度颜色模型。此外,形态学操作和分水岭变换结合加权外部距离变换来分离聚类对象。然后使用分类器算法检测(并因此计数)阳性(amastigote)对象,其中的选择以及要使用的特征的定义也是本研究的一部分。利用MATLAB进行了系统的开发。
■根据灵敏度评估结果,精度,和F测度,并提出了该方法的良好有效性。
■该系统可以通过使用自动图像分析技术对大量的amastigotes图像进行计数来帮助研究人员。
UNASSIGNED: Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania and has a high prevalence and impact on global health. Currently, the available drugs for its treatment have drawbacks, such as high toxicity, resistance of the parasite, and high cost. Therefore, the search for new, more effective, and safe drugs is a priority. The effectiveness of an anti-leishmanial drug is analyzed through in vitro studies in which a technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is slow, laborious, and prone to errors.
UNASSIGNED: To develop a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques.
UNASSIGNED: Segmentation of objects in the microscope image that might be Leishmania amastigotes was performed using the multilevel Otsu method on the saturation component of the hue, saturation, and intensity color model. In addition, morphological operations and the watershed transform combined with the weighted external distance transform were used to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, the selection of which as well as the definition of the features to be used were also part of this research. MATLAB was used for the development of the system.
UNASSIGNED: The results were evaluated in terms of sensitivity, precision, and the F-measure and suggested a favorable effectiveness of the proposed method.
UNASSIGNED: This system can help researchers by allowing large volumes of images of amastigotes to be counted using an automatic image analysis technique.