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%.

Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images / Di Cataldo, Santa; Bottino, Andrea Giuseppe; Ficarra, Elisa; Macii, Enrico. - (2012), pp. 3349-3352. (Intervento presentato al convegno 21st International Conference on Pattern Recognition, ICPR 2012 tenutosi a Tsukuba, Japan nel November 11-15, 2012).

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

FICARRA, ELISA;
2012

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%.
2012
21st International Conference on Pattern Recognition, ICPR 2012
Tsukuba, Japan
November 11-15, 2012
3349
3352
Di Cataldo, Santa; Bottino, Andrea Giuseppe; Ficarra, Elisa; Macii, Enrico
Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images / Di Cataldo, Santa; Bottino, Andrea Giuseppe; Ficarra, Elisa; Macii, Enrico. - (2012), pp. 3349-3352. (Intervento presentato al convegno 21st International Conference on Pattern Recognition, ICPR 2012 tenutosi a Tsukuba, Japan nel November 11-15, 2012).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1240371
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