Guven Mustafa, Cengizler Caglar
Faculty of Engineering and Architecture Department of Biomedical Engineering, Cukurova University, Balcalı, 01330 Adana, Turkey.
Biomed Eng Online. 2014 Dec 9;13:159. doi: 10.1186/1475-925X-13-159.
The extraction of overlapping cell nuclei is a critical issue in automated diagnosis systems. Due to the similarities between overlapping and malignant nuclei, misclassification of the overlapped regions can affect the automated systems' final decision. In this paper, we present a method for detecting overlapping cell nuclei in Pap smear samples.
Judgement about the presence of overlapping nuclei is performed in three steps using an unsupervised clustering approach: candidate nuclei regions are located and refined with morphological operations; key features are extracted; and candidate nuclei regions are clustered into two groups, overlapping or non-overlapping, A new combination of features containing two local minima-based and three shape-dependent features are extracted for determination of the presence or absence of overlapping. F1 score, precision, and recall values are used to evaluate the method's classification performance.
In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours. Experimental results indicate that applied morphological operations can locate most of the nuclei and produces accurate boundaries. Independent features significance test indicates that our feature combination is significant for overlapping nuclei. Comparisons of the classification results of a fuzzy clustering algorithm and a non-fuzzy clustering algorithm show that the fuzzy approach would be a more convenient mechanism for classification of overlapping.
The main contribution of this study is the development of a decision mechanism for identifying overlapping nuclei to further improve the extraction process with respect to the segmentation of interregional borders, nuclei area, and radius. Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.
重叠细胞核的提取是自动诊断系统中的一个关键问题。由于重叠细胞核和恶性细胞核之间存在相似性，重叠区域的错误分类会影响自动系统的最终决策。在本文中，我们提出了一种检测巴氏涂片样本中重叠细胞核的方法。
使用无监督聚类方法分三步判断重叠细胞核的存在：通过形态学操作定位并细化候选细胞核区域；提取关键特征；将候选细胞核区域聚类为重叠或不重叠两组。提取包含两个基于局部最小值和三个形状相关特征的新特征组合，以确定是否存在重叠。使用F1分数、精确率和召回率值来评估该方法的分类性能。
为了进行评估，我们将所提出系统的分割结果与经验轮廓进行了比较。实验结果表明，应用的形态学操作可以定位大多数细胞核并产生准确的边界。独立特征显著性检验表明，我们的特征组合对重叠细胞核具有显著性。模糊聚类算法和非模糊聚类算法分类结果的比较表明，模糊方法对于重叠分类是一种更方便的机制。
本研究的主要贡献是开发了一种识别重叠细胞核的决策机制，以进一步改进区域间边界、细胞核面积和半径分割方面的提取过程。实验结果表明，我们提出的具有特征组合的无监督方法在检测重叠细胞核方面具有可接受的性能。
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