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Automated Cells Counting for Leukaemia and Malaria Detection Based on RGB and HSV Colour Spaces Analysis

机译:基于RGB和HSV颜色空间分析的用于白血病和疟疾检测的自动细胞计数

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There are various types of diseases which are originated from the blood, for example leukaemia, malaria and anaemia. Leukaemia is a cancer which starts in blood forming tissues usually the bone marrow. On the other hand, malaria is transmitted through the bite of infected mosquito that carrying the Plasmodium parasite. Haematologists needs to perform the WBCs count in order to determine if a person has leukaemia and parasite count to check for the malaria density. However, the conventional procedure is very vulnerable due to human error and large time consumption. As a solution, this study proposes automated cells counting for leukaemia and malaria detection by analyzing the best colour component of RGB and HSV colour spaces. To obtain the cells counting result, there are several image processing steps to be implemented; (1) image acquisition by capturing the leukaemia blood samples using a computerized Leica DLMA 1200 digital microscope, (2) colour conversion from RGB to single colour component of RGB and HSV, (3) image segmentation using Otsu thresholding, (4) removing of unwanted regions and, (5) cells counting process. Overall, segmentation using green component of RGB colour space has proven to be the best in segmenting leukaemia images with 83.84% while saturation component of HSV colour space hold the highest accuracy for malaria images with 89.87%. Conclusively, this research is expected to help improving the detection phase of malaria and leukaemia diseases by overcome problems that been identify in this research.
机译:存在多种源自血液的疾病,例如白血病,疟疾和贫血。白血病是一种癌症,起源于通常是骨髓的血液形成组织。另一方面,疟疾是通过携带疟原虫寄生虫的被感染蚊子叮咬传播的。血液学家需要进行WBC计数,以确定一个人是否患有白血病和寄生虫计数,以检查疟疾密度。然而,由于人为错误和大量时间消耗,传统程序非常脆弱。作为解决方案,这项研究提出了通过分析RGB和HSV颜色空间的最佳颜色成分来自动计数白血病和疟疾的细胞。为了获得细胞计数结果,需要执行几个图像处理步骤。 (1)使用计算机化的Leica DLMA 1200数字显微镜通过捕获白血病血样来获取图像;(2)从RGB到RGB和HSV的单色分量进行颜色转换;(3)使用Otsu阈值化进行图像分割;(4)去除(5)细胞计数过程。总体而言,事实证明,使用RGB色彩空间的绿色成分进行分割是分割白血病图像的最佳方法,占83.84%,而HSV色彩空间的饱和度成分对疟疾图像的准确性最高,达89.87%。结论是,这项研究有望克服本研究中发现的问题,从而有助于改善疟疾和白血病疾病的检测阶段。

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