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EnglishThe 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. - STAMPA. - (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.
| Titolo: | Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images | |
| Autori: | ||
| Data di pubblicazione: | 2012 | |
| Abstract: | The analysis of anti-nuclear antibodies in HEp-2 cells by indirect immunofluorescence (IIF) is f...undamental 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%. | |
| ISBN: | 978-4-9906441-0-9 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| ICPR2012_PORTO.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2501659
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