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Mining knowledge for HEp-2 cell image classification
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DOI：10.1016/S0933-3657(02)00057-X.png)
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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.
Keyword:
image mining
data mining
medical diagnosis
HEp-2 cell classification
fluorescence image analysis
decision tree induction
texture classification
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