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Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images

Abstract: The analysis of anti-nuclear antibodies in HEp-2 cells by indirect immunofluorescence (IIF) is fundamental for the diagnosis of important immune pathologies; in particular, classifying the staining pattern of the cell is critical for the differential diagnosis of several types of diseases. Current tests based on human evaluation are time-consuming and suffer from very high variability, which impacts on the reliability of the results. As a solution to this problem, in this work we propose a technique that performs automated classification of the staining pattern. Our method combines textural feature extraction and a two-step feature selection scheme to select a limited number of image attributes that are best suited to the classification purpose and then recognizes the staining pattern by means of a Support Vector Machine module. Experiments on IIF images showed that our method is able to identify staining patterns with average accuracy of about 87%.


Citation:

Di Cataldo, Santa; Bottino, Andrea Giuseppe; Ficarra, Elisa; Macii, Enrico "Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images" Proc. of 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, jpn, pp. 3349 -3352 , November 11-15, 2012, 2012

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