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

Siyamalan Manivannan; Wenqi Li; Shazia Akbar; Ruixuan Wang; Jianguo Zhang; Stephen J. McKenna

doi:10.1109/I3A.2014.20

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

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 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 language	English
Title of host publication	Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014
Publisher	IEEE Computer Society
Pages	41-44
Number of pages	4
ISBN (Print)	9781479942527
DOIs	https://doi.org/10.1109/I3A.2014.20
Publication status	Published - 2014
Event	13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images - Stockholm Waterfront, Stockholm, Sweden Duration: 24 Aug 2014 → 24 Aug 2014 http://i3a2014.unisa.it/

Workshop

Workshop	13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images
Country/Territory	Sweden
City	Stockholm
Period	24/08/14 → 24/08/14
Internet address	http://i3a2014.unisa.it/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/I3A.2014.20

Cite this

@inproceedings{1752b8a27eb64fda98967b0527abacc0,

title = "HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM",

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\%.",

author = "Siyamalan Manivannan and Wenqi Li and Shazia Akbar and Ruixuan Wang and Jianguo Zhang and McKenna, \{Stephen J.\}",

year = "2014",

doi = "10.1109/I3A.2014.20",

language = "English",

isbn = "9781479942527",

pages = "41--44",

booktitle = "Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014",

publisher = "IEEE Computer Society",

note = "13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images ; Conference date: 24-08-2014 Through 24-08-2014",

url = "http://i3a2014.unisa.it/",

}

Manivannan, S, Li, W, Akbar, S, Wang, R, Zhang, J & McKenna, SJ 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. IEEE Computer Society, pp. 41-44, 13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images, Stockholm, Sweden, 24/08/14. https://doi.org/10.1109/I3A.2014.20

HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM. / Manivannan, Siyamalan (Lead / Corresponding author); Li, Wenqi; Akbar, Shazia et al.
Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014. IEEE Computer Society, 2014. p. 41-44.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM

AU - Manivannan, Siyamalan

AU - Li, Wenqi

AU - Akbar, Shazia

AU - Wang, Ruixuan

AU - Zhang, Jianguo

AU - McKenna, Stephen J.

PY - 2014

Y1 - 2014

N2 - 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%.

AB - 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%.

UR - https://www.scopus.com/pages/publications/84919433668

U2 - 10.1109/I3A.2014.20

DO - 10.1109/I3A.2014.20

M3 - Conference contribution

AN - SCOPUS:84919433668

SN - 9781479942527

SP - 41

EP - 44

BT - Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014

PB - IEEE Computer Society

T2 - 13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images

Y2 - 24 August 2014 through 24 August 2014

ER -

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

Abstract

Workshop

UN SDGs

ASJC Scopus subject areas

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Analysis of Colorectal Polyps in Optical Projection Tomography

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

Abstract

Workshop

UN SDGs

ASJC Scopus subject areas

Access to Document

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Fingerprint

Student theses

Analysis of Colorectal Polyps in Optical Projection Tomography

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