Automatic cervical cell segmentation and classification in Pap smears
Automatic cervical cell segmentation and classification in Pap smears
复制标题
DOI：
10.1016/j.cmpb.2013.12.012
复制
发表时间：
2014-02-01
影响因子：
6.1
通讯作者：
Auephanwiriyakul, Sansanee
中科院分区：
文献类型：
--
作者：
Chankong, Thanatip;Theera-Umpon, Nipon;Auephanwiriyakul, Sansanee
宫颈癌是全球女性癌症死亡的主要原因之一。如果患者在癌前病变阶段或更早被诊断出来，这种疾病是可以治愈的。筛查中广泛使用的一种常见体检技术是巴氏试验或巴氏试验。本研究提出了一种宫颈癌细胞的自动分割和分类方法。使用模糊C均值(FCM)聚类技术将单细胞图像分割为细胞核、细胞质和背景。ERUDIT和LCH数据集中的四种细胞类型，即正常、低级别鳞状上皮内病变(Lsil)、高度鳞状上皮内病变(HSIL)和鳞状细胞癌(SCC)被考虑。两类问题可以通过将最后3类归为一个异常类来实现。然而，Herlev数据集由7个细胞类别组成，即浅表鳞状细胞、中度鳞状细胞、柱状细胞、轻度不典型增生、中度不典型增生、重度不典型增生和原位癌。这7个类也可以组合成一个2类问题。在贝叶斯分类器、线性判别分析(LDA)、K近邻(KNN)、人工神经网络(ANN)和支持向量机(SVM)等5种分类器上对这3个数据集进行了测试。对于ERUDIT数据集，具有5个核特征的ANN在4类和2类问题上的准确率分别为96.20%和97.83%。对于Herlev数据集，具有9个单元特征的ANN对7类和2类问题的准确率分别为93.78%和99.27%。对于LCH数据集，具有9个单元特征的ANN对4类问题和2类问题的准确率分别为95.00%和97.00%。将该方法的分割和分类性能与硬C均值聚类和分水岭方法进行了比较。结果表明，所提出的自动方法取得了很好的效果，并且优于同类方法。(C)2013爱思唯尔爱尔兰有限公司。保留所有权利。
Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts. (C) 2013 Elsevier Ireland Ltd. All rights reserved.