Mining knowledge for HEp-2 cell image classification
Mining knowledge for HEp-2 cell image classification
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DOI：
10.1016/s0933-3657(02)00057-x
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发表时间：
2002-09-01
影响因子：
7.5
通讯作者：
M端ller, B
中科院分区：
文献类型：
--
作者：
Perner, P;Perner, H;M端ller, B
HEP-2细胞用于鉴定抗核自身抗体(ANA)。它们可以识别30多种不同的核型和细胞质模式，这些模式是由100多种不同的自身抗体提供的。最近，人类用显微镜检查载玻片，人工完成了图案的识别。在本文中，我们介绍了利用图像分析和数据挖掘技术对细胞进行分析和分类的结果。从人类操作员的知识获取过程出发，提出了一种图像分析和特征提取算法。数据集的收集是基于专家的图像阅读和基于自动提取的特征完成的。为每个条目建立一个包含132个特征的数据集，并将其提供给数据挖掘算法，以在这个庞大的特征集中找出相关特征并构造分类知识。采用交叉验证的方法对分类器进行评价。结果使专家对所需特征和分类知识有了新的认识，表明了自动检测系统的可行性。(C)2002 Elsevier Science B.V.保留所有权利。
HEp-2 cells are used for the identification of antinuclear autoantibodies (ANAs). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. The identification of the patterns has recently been done manually by a human inspecting the slides with a microscope. In this paper, we present results on the analysis and classification of cells using image analysis and data mining techniques. Starting from a knowledge acquisition process with a human operator, we developed an image analysis and feature extraction algorithm. The collection of the dataset was done based on an expert's image reading and based on the automatic extracted features. A dataset containing 132 features for each entry was set up and given to a data mining algorithm to find out the relevant features among this large feature set and to construct the classification knowledge. The classifier was evaluated by cross validation. The results gave the expert new insights into the necessary features and the classification knowledge and show the feasibility of an automated inspection system. (C) 2002 Elsevier Science B.V. All rights reserved.