Authors:
Ihtesham Ul Islam
;
Santa Di Cataldo
;
Andrea Bottino
;
Elisa Ficarra
and
Enrico Macii
Affiliation:
Politecnico di Torino, Italy
Keyword(s):
HEp-2 cells, Indirect ImmunoFluorescence, Staining Pattern Classification, Support Vector Machines, Subclass Discriminant Analysis, Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Image Analysis
;
Immuno- and Chemo-Informatics
;
Pattern Recognition, Clustering and Classification
Abstract:
Anti-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 dif
ferent 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.
(More)