HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

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

doi:10.1109/I3A.2014.18

HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

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

35 Citations (Scopus)

Abstract

We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.

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	37-40
Number of pages	4
ISBN (Print)	9781479942527
DOIs	https://doi.org/10.1109/I3A.2014.18
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/

Keywords

HEp-2 Cell Classification
multi-resolution local patterns
pattern recognition

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

UN SDGs

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

Access to Document

10.1109/I3A.2014.18

Cite this

@inproceedings{f2f08febf63645e99f3e46a0b9f1cb46,

title = "HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs",

abstract = "We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.",

keywords = "HEp-2 Cell Classification, multi-resolution local patterns, pattern recognition",

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.2014.18",

language = "English",

isbn = "9781479942527",

pages = "37--40",

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 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs. in Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014. IEEE Computer Society, pp. 37-40, 13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images, Stockholm, Sweden, 24/08/14. https://doi.org/10.1109/I3A.2014.18

HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs. / 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. 37-40.

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

TY - GEN

T1 - HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

AU - Manivannan, Siyamalan

AU - Li, Wenqi

AU - Akbar, Shazia

AU - Wang, Ruixuan

AU - Zhang, Jianguo

AU - McKenna, Stephen J.

PY - 2014

Y1 - 2014

N2 - We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.

AB - We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.

KW - HEp-2 Cell Classification

KW - multi-resolution local patterns

KW - pattern recognition

UR - http://www.scopus.com/inward/record.url?scp=84919457731&partnerID=8YFLogxK

U2 - 10.1109/I3A.2014.18

DO - 10.1109/I3A.2014.18

M3 - Conference contribution

AN - SCOPUS:84919457731

SN - 9781479942527

SP - 37

EP - 40

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 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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Analysis of Colorectal Polyps in Optical Projection Tomography

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HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

Abstract

Workshop

Keywords

ASJC Scopus subject areas

UN SDGs

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Student theses

Analysis of Colorectal Polyps in Optical Projection Tomography

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