Chosun University, Department of Computer Engineering, Dong-gu, Gwangju, Republic of KoreabChosun University, Center for Holographic Imaging Informatics, Dong-gu, Gwangju, Republic of Korea.
J Biomed Opt. 2017 Jul 1;22(7):76015. doi: 10.1117/1.JBO.22.7.076015.
We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.
我们提出了一种无监督聚类方法，用于将数字全息显微镜获得的红细胞定量相位图像中提取的人类红细胞（RBC）自动分为具有规则形状的三个 RBC 簇，包括双凹形、口形和棘形红细胞。我们选择了一些与 RBC 形态相关的良好特征，例如 RBC 平均厚度、球形系数和平均红细胞体积，以及聚类方法，包括基于密度的空间聚类应用程序与噪声、k-medoids 和 k-means，应用于形态特征集。使用一组三维特征对 RBC 的聚类结果与一组二维特征进行了比较。我们的实验结果表明，通过利用引入的特征集，可以将两组双凹形 RBC 和老年 RBC（遭受棘形红细胞过程）完美聚类。此外，通过增加聚类的数量，可以以自动化的无监督方式有效地对三种 RBC 类型进行聚类，具有很高的准确性。聚类技术的性能评估表明，它们可以帮助血液学家进行进一步诊断。
