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Extraction of color features in the spectral domain to recognize centroblasts in histopathology.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2009; Vol. 2009, pp. 3685-8. - Publication Year :
- 2009
-
Abstract
- In this paper, we are proposing a novel automated method to recognize centroblast (CB) cells from non-centroblast (non-CB) cells for computer-assisted evaluation of follicular lymphoma tissue samples. The method is based on training and testing of a quadratic discriminant analysis (QDA) classifier. The novel aspects of this method are the identification of the CB object with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract color texture features. Both geometric and texture features are used to achieve the classification. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the performance of the system significantly. The implemented method can identify centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56%.
- Subjects :
- Algorithms
Color
Cytoplasm metabolism
Discriminant Analysis
Humans
Lymphoma metabolism
Models, Statistical
Multivariate Analysis
Principal Component Analysis
Reproducibility of Results
Cytological Techniques
Histological Techniques
Histology instrumentation
Lymphoma pathology
Lymphoma, Follicular diagnosis
Lymphoma, Follicular pathology
Subjects
Details
- Language :
- English
- ISSN :
- 2375-7477
- Volume :
- 2009
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 19965003
- Full Text :
- https://doi.org/10.1109/IEMBS.2009.5334727