HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM

Siyamalan Manivannan (Lead / Corresponding author), Wenqi Li, Shazia Akbar, Ruixuan Wang, Jianguo Zhang, Stephen J. McKenna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

A pattern recognition system was developed to classify immunofluorescence images of HEp-2 specimens into seven classes: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. Root-SIFT features together with multi-resolution local patterns were used to capture local shape and texture information. Sparse coding with max-pooling was applied to get an image representation from these local features. Specimens were classified using a linear support vector machine. Leave-one-specimen-out experiments on the I3A Contest Task 2 data set predicted a mean class accuracy of 89.9%.

Original languageEnglish
Title of host publicationProceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014
PublisherIEEE Computer Society
Pages41-44
Number of pages4
ISBN (Print)9781479942527
DOIs
Publication statusPublished - 2014
Event13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images - Stockholm Waterfront, Stockholm, Sweden
Duration: 24 Aug 201424 Aug 2014
http://i3a2014.unisa.it/

Workshop

Workshop13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images
CountrySweden
CityStockholm
Period24/08/1424/08/14
Internet address

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Cite this

Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., & McKenna, S. J. (2014). HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM. In Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014 (pp. 41-44). IEEE Computer Society. https://doi.org/10.1109/I3A.2014.20