HEp-2 cell classification using multilevel wavelet decomposition

Ranveer Katyal; Manohar Kuse; Subrat Kumar Dash

doi:10.1109/TENCONSpring.2014.6863014

HEp-2 cell classification using multilevel wavelet decomposition

Ranveer Katyal, Manohar Kuse, Subrat Kumar Dash

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

Abstract

The analysis of anti-nuclear antibodies in HEp- 2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.

Original language	English
Title of host publication	IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	147-150
Number of pages	4
ISBN (Electronic)	9781479920280
DOIs	https://doi.org/10.1109/TENCONSpring.2014.6863014
Publication status	Published - 23 Jul 2014
Externally published	Yes
Event	2014 IEEE Region 10 Symposium, IEEE TENSYMP 2014 - Kuala Lumpur, Malaysia Duration: 14 Apr 2014 → 16 Apr 2014

Publication series

Name	IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium

Conference

Conference	2014 IEEE Region 10 Symposium, IEEE TENSYMP 2014
Country/Territory	Malaysia
City	Kuala Lumpur
Period	14/04/14 → 16/04/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

HEp-2 cell classification
Multi-level wavelet decomposition
Wavelet texture representation

Access to Document

10.1109/TENCONSpring.2014.6863014

Cite this

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title = "HEp-2 cell classification using multilevel wavelet decomposition",

abstract = "The analysis of anti-nuclear antibodies in HEp- 2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.",

keywords = "HEp-2 cell classification, Multi-level wavelet decomposition, Wavelet texture representation",

author = "Ranveer Katyal and Manohar Kuse and Dash, \{Subrat Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Region 10 Symposium, IEEE TENSYMP 2014 ; Conference date: 14-04-2014 Through 16-04-2014",

year = "2014",

month = jul,

day = "23",

doi = "10.1109/TENCONSpring.2014.6863014",

language = "English",

series = "IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium",

}

Katyal, R, Kuse, M & Dash, SK 2014, HEp-2 cell classification using multilevel wavelet decomposition. in IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium., 6863014, IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium, Institute of Electrical and Electronics Engineers Inc., pp. 147-150, 2014 IEEE Region 10 Symposium, IEEE TENSYMP 2014, Kuala Lumpur, Malaysia, 14/04/14. https://doi.org/10.1109/TENCONSpring.2014.6863014

HEp-2 cell classification using multilevel wavelet decomposition. / Katyal, Ranveer; Kuse, Manohar; Dash, Subrat Kumar.
IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium. Institute of Electrical and Electronics Engineers Inc., 2014. p. 147-150 6863014 (IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

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AU - Katyal, Ranveer

AU - Kuse, Manohar

AU - Dash, Subrat Kumar

PY - 2014/7/23

Y1 - 2014/7/23

N2 - The analysis of anti-nuclear antibodies in HEp- 2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.

AB - The analysis of anti-nuclear antibodies in HEp- 2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.

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ER -

HEp-2 cell classification using multilevel wavelet decomposition

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