Cell morphology based classification for red cells in blood smear images
Cell morphology based classification for red cells in blood smear images
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DOI：
10.1016/j.patrec.2014.06.010
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发表时间：
2014-11-01
影响因子：
5.1
通讯作者：
Chen, Yi-Ping Phoebe
中科院分区：
文献类型：
--
作者：
Lee, Howard;Chen, Yi-Ping Phoebe
红细胞是最常见的血细胞类型，负责向身体组织输送氧气。红细胞异常可能改变红细胞的物理特性或缩短其寿命，并可能导致中风或贫血。在本文中，我们提出了一种基于混合神经网络的分类器，该分类器利用从红细胞图像中提取的视觉信息来判断红细胞是否正常或异常。基于特征属性，将视觉特征聚类为两大类，即形状聚类和纹理聚类。采用多输入层并行级联结构对输入特征簇进行处理。我们的实验结果表明，与使用最近的特征选择算法的单输入层分类器相比，我们提出的系统在分类精度方面有显着提高。(C) 2014 Elsevier B.V.版权所有
Red blood cells are the most common type of blood cell and are responsible of delivering oxygen to the body tissues. Abnormalities in red blood cell may change the physical properties of the red cell or shorten its life spend, and may lead to stroke or anemia. In this paper, we proposed a hybrid neural network based classifier, which utilize the visual information extracted from the red blood cell images to determine whether a red cell is normal or abnormal. Based on the feature properties, we clustered the visual features into two main groups, namely shape and texture cluster groups. The input feature clusters were processed using parallel and cascade architecture with multiple input layers. Our experimental result has shown significant improvement in classification accuracy in our proposed system as compared to the single input layer classifier with recent feature selection algorithms. (C) 2014 Elsevier B.V. All rights reserved.