Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor
Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor
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DOI：
10.1016/j.compmedimag.2020.101813
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发表时间：
2021-01-01
影响因子：
5.7
通讯作者：
Khan, Raja Jalees ul Hussen
中科院分区：
文献类型：
--
作者：
Rahman, Samiur;Azam, Bakht;Khan, Raja Jalees ul Hussen
血液涂片图像中红细胞的解剖结构在多种疾病的检测中起着重要的作用。自动化的基于图像的技术是快速和准确的血细胞形态分析，可以节省病理学家以及患者的时间。在本文中，我们提出了一种新的方法，分割和识别不同的红细胞在一个给定的血液涂片图像。在所提出的方法中，中央苍白和整个细胞的信息使用后，使用颜色处理，其次是双阈值的血液涂片图像。计算细胞的形状和大小方差，用于识别外周血涂片图像中的异常。我们使用交叉验证精度加权概率集成（CAWPE）。它是一种异构的集成技术，几乎等同于平均显着更好的分类器（关于错误和概率估计）相比，广泛的潜在的父分类器产生的分类器。所提出的方法进行了测试3组图像。这些图像是由当地政府医院的专家病理学家准备的。每个图像集都有不同的摄影条件。该方法被发现在结果方面是准确的，更接近真实情况。所提出的方法的平均准确率为97%的单细胞和96%的重叠细胞的分割。准确度的方差（sigma（2））为3.5，偏差（sigma）为1.87。
The anatomy of red blood cells (RBCs) in blood smear images plays an important role in the detection of several diseases. The automated image-based technique is fast and accurate for the analysis of blood cells morphology that can save time of both pathologists as well as that of patients. In this paper, we propose a novel method which segment and identify varied RBCs in a given blood smear images. In the proposed method, the central pallor and whole cell information are used, after using color processing followed by double thresholding of blood smear images. The shape and size variances of cells are calculated for the identification of abnormalities in peripheral blood smear images. We used cross-validation accuracy weighted probabilistic ensemble (CAWPE). It is a heterogeneous ensembling technique of nearly equivalent classifiers produced on averagely significant better classifiers (regarding errors and probability estimates) as compared to a wide range of potential parent classifiers. The proposed method is tested on 3 sets of images. The sets of images were prepared in a local government hospital by expert pathologists. Each image set has varied photographic conditions. The method was found accurate in term of results, closer to the ground truth. The average accuracy of the proposed method is 97% for the segmentation of single cells and 96% for overlapped cells. The variance (sigma(2)) of accuracy is 3.5 and the deviation (sigma) is 1.87.