Detection of red and white blood cells from microscopic blood images using a region proposal approach
Detection of red and white blood cells from microscopic blood images using a region proposal approach
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DOI：
10.1016/j.compbiomed.2019.103530
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发表时间：
2020-01-01
影响因子：
7.7
通讯作者：
Putzu, Lorenzo
中科院分区：
文献类型：
--
作者：
Di Ruberto, Cecilia;Loddo, Andrea;Putzu, Lorenzo
在本文中，我们提出了一种新的和有效的方法来检测和定量红细胞和白细胞的显微血液图像。使用细胞计数器或流式细胞仪的实验室测试可以快速执行全血细胞计数（CBC）。尽管如此，仍然需要手工血液涂片检查，以便对计数器结果进行人工检查并监测正在接受治疗的患者。此外，它允许描述细胞的外观以及任何异常。然而，手工分析是冗长和重复的，其结果可能是主观的和容易出错的。相反，通过使用图像处理技术，所提出的系统是完全自动化的。主要的努力是致力于实现高精度，并找到一种方法来克服计算机辅助方法遇到的血液涂片图像条件的典型差异。它基于边缘盒方法，这被认为是最先进的区域建议方法。通过将基于知识的约束结合到边缘盒的检测过程中，我们可以快速有效地找到细胞提案。我们在急性淋巴细胞白血病图像数据库（ALL-IDB）和疟疾寄生虫图像数据库（MPIDS）上测试了该方法，急性淋巴细胞白血病图像数据库（ALL-IDB）是一个众所周知的用于白血病检测的公共数据集，而疟疾寄生虫图像数据库（MPIDS）是最近提出的用于疟疾检测的数据集。在这两种情况下的实验结果都非常出色，在ALL-IDB上的表现优于最先进的技术，并在MP-IDB上创建了一个强大的基线，表明所提出的方法可以在不同的数据集和不同类型的图像上很好地工作。
In this paper, we propose a novel and efficient method for detecting and quantifying red and white blood cells from microscopic blood images. Laboratory tests that use a cell counter or a flow cytometer can perform a complete blood count (CBC) rapidly. Nonetheless, a manual blood smear inspection is still needed, both to have a human check on the counter results and to monitor patients under therapy. Moreover, it allows for describing the cells' appearance as well as any abnormalities. However, manual analysis is lengthy and repetitive, and its result can be subjective and error-prone. In contrast, by using image processing techniques, the proposed system is entirely automated. The main effort is devoted to both achieving high accuracy and finding a way to overcome the typical differences in the condition of blood smear images that computer-aided methods encounter. It is based on the Edge Boxes method, which is considered a state-of-art region proposal approach. By incorporating knowledge-based constraints into the detection process using Edge Boxes, we can find cell proposals rapidly and efficiently. We tested the proposed approach on the Acute Lymphoblastic Leukaemia Image Database (ALL-IDB), a well-known public dataset proposed for leukaemia detection, and the Malaria Parasite Image Database (MPIDS), a recently proposed dataset for malaria detection. Experimental results were excellent in both cases, outperforming the state-of-the-art on ALL-IDB and creating a strong baseline on MP-IDB, demonstrating that the proposed method can work well on different datasets and different types of images.