nti-nuclear antibodies test is based on the visual evaluation of the intensity and staining pattern in HEp-2 cell slides by means of indirect immunofluorescence (IIF) imaging, revealing the presence of autoantibodies responsible for important immune pathologies. In particular, the categorization of the staining pattern is crucial for differential diagnosis, because it provides information about autoantibodies type. Their manual classification is very time-consuming and not very reliable, since it depends on the subjectivity and on the experience of the specialist. This motivates the growing demand for computer-aided solutions able to perform staining pattern classification in a fully automated way. In this work we compare two classification techniques, based respectively on Support Vector Machines and Subclass Discriminant Analysis. A set of textural features characterizing the available samples are first extracted. Then, a feature selection scheme is applied in order to produce different datasets, containing a limited number of image attributes that are best suited to the classification purpose. Experiments on IIF images showed that our computer-aided method is able to identify staining patterns with an average accuracy of about 91% and demonstrate, in this specific problem, a better performance of Subclass Discriminant Analysis with respect to Support Vector Machines.

Classification of HEp-2 staining patterns in ImmunoFluorescence images. Comparison of Support Vector Machines and Subclass Discriminant Analysis strategies / UL-ISLAM, Ihtesham; DI CATALDO, Santa; Bottino, ANDREA GIUSEPPE; Ficarra, Elisa; Macii, Enrico. - (2013), pp. 53-61. (Intervento presentato al convegno International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013 tenutosi a Barcelona (SP) nel 11-14 February 2013).

Classification of HEp-2 staining patterns in ImmunoFluorescence images. Comparison of Support Vector Machines and Subclass Discriminant Analysis strategies

FICARRA, ELISA;
2013

Abstract

nti-nuclear antibodies test is based on the visual evaluation of the intensity and staining pattern in HEp-2 cell slides by means of indirect immunofluorescence (IIF) imaging, revealing the presence of autoantibodies responsible for important immune pathologies. In particular, the categorization of the staining pattern is crucial for differential diagnosis, because it provides information about autoantibodies type. Their manual classification is very time-consuming and not very reliable, since it depends on the subjectivity and on the experience of the specialist. This motivates the growing demand for computer-aided solutions able to perform staining pattern classification in a fully automated way. In this work we compare two classification techniques, based respectively on Support Vector Machines and Subclass Discriminant Analysis. A set of textural features characterizing the available samples are first extracted. Then, a feature selection scheme is applied in order to produce different datasets, containing a limited number of image attributes that are best suited to the classification purpose. Experiments on IIF images showed that our computer-aided method is able to identify staining patterns with an average accuracy of about 91% and demonstrate, in this specific problem, a better performance of Subclass Discriminant Analysis with respect to Support Vector Machines.
2013
International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013
Barcelona (SP)
11-14 February 2013
53
61
UL-ISLAM, Ihtesham; DI CATALDO, Santa; Bottino, ANDREA GIUSEPPE; Ficarra, Elisa; Macii, Enrico
Classification of HEp-2 staining patterns in ImmunoFluorescence images. Comparison of Support Vector Machines and Subclass Discriminant Analysis strategies / UL-ISLAM, Ihtesham; DI CATALDO, Santa; Bottino, ANDREA GIUSEPPE; Ficarra, Elisa; Macii, Enrico. - (2013), pp. 53-61. (Intervento presentato al convegno International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013 tenutosi a Barcelona (SP) nel 11-14 February 2013).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1240384
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