A novel adaptive local thresholding approach for segmentation of HEp-2 cell images

Xiande Zhou; Yuexiang Li; Linlin Shen

doi:10.1109/SIPROCESS.2016.7888247

A novel adaptive local thresholding approach for segmentation of HEp-2 cell images

Xiande Zhou
, Yuexiang Li
, Linlin Shen

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

The patterns of Human Epithelial type 2 (HEp-2) cell provide useful information for the diagnosis of systemic autoimmune diseases. However, the recognition of cell patterns requires manual annotation by experienced physicians, which is subject to inter-observer variability. Therefore, an automatic diagnosis system is desirable. As the crucial pre-processing step for cell pattern recognition, the performance of cell segmentation is crucial. In this paper, a novel adaptive local thresholding approach is proposed to solve the issue. The approach divides cell images into overlapping sub-images and applies adaptive threshold estimator to each of them. The ICPR 2014 HEp-2 cell datasets are employed to assess the segmentation performance of our framework. The results show that the system achieves an average segmentation accuracy of 66.95%, which outperforms the typical thresholding approaches.

Original language	English
Title of host publication	2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	174-178
Number of pages	5
ISBN (Electronic)	9781509023769
DOIs	https://doi.org/10.1109/SIPROCESS.2016.7888247
Publication status	Published - 27 Mar 2017
Externally published	Yes
Event	2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 - Beijing, China Duration: 13 Aug 2016 → 15 Aug 2016

Publication series

Name	2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

Conference

Conference	2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
Country/Territory	China
City	Beijing
Period	13/08/16 → 15/08/16

UN SDGs

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

SDG 3 Good Health and Well-being

Free Keywords

Adaptive thresholding
Cell images
Local thresholding
THRESHOLD estimator

ASJC Scopus subject areas

Signal Processing

Access to Document

10.1109/SIPROCESS.2016.7888247

Cite this

Zhou, X., Li, Y., & Shen, L. (2017). A novel adaptive local thresholding approach for segmentation of HEp-2 cell images. In 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 (pp. 174-178). Article 7888247 (2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIPROCESS.2016.7888247

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title = "A novel adaptive local thresholding approach for segmentation of HEp-2 cell images",

abstract = "The patterns of Human Epithelial type 2 (HEp-2) cell provide useful information for the diagnosis of systemic autoimmune diseases. However, the recognition of cell patterns requires manual annotation by experienced physicians, which is subject to inter-observer variability. Therefore, an automatic diagnosis system is desirable. As the crucial pre-processing step for cell pattern recognition, the performance of cell segmentation is crucial. In this paper, a novel adaptive local thresholding approach is proposed to solve the issue. The approach divides cell images into overlapping sub-images and applies adaptive threshold estimator to each of them. The ICPR 2014 HEp-2 cell datasets are employed to assess the segmentation performance of our framework. The results show that the system achieves an average segmentation accuracy of 66.95\%, which outperforms the typical thresholding approaches.",

keywords = "Adaptive thresholding, Cell images, Local thresholding, THRESHOLD estimator",

author = "Xiande Zhou and Yuexiang Li and Linlin Shen",

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year = "2017",

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language = "English",

series = "2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016",

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

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}

Zhou, X, Li, Y & Shen, L 2017, A novel adaptive local thresholding approach for segmentation of HEp-2 cell images. in 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016., 7888247, 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016, Institute of Electrical and Electronics Engineers Inc., pp. 174-178, 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016, Beijing, China, 13/08/16. https://doi.org/10.1109/SIPROCESS.2016.7888247

A novel adaptive local thresholding approach for segmentation of HEp-2 cell images. / Zhou, Xiande; Li, Yuexiang; Shen, Linlin.
2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 174-178 7888247 (2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A novel adaptive local thresholding approach for segmentation of HEp-2 cell images

AU - Zhou, Xiande

AU - Li, Yuexiang

AU - Shen, Linlin

PY - 2017/3/27

Y1 - 2017/3/27

N2 - The patterns of Human Epithelial type 2 (HEp-2) cell provide useful information for the diagnosis of systemic autoimmune diseases. However, the recognition of cell patterns requires manual annotation by experienced physicians, which is subject to inter-observer variability. Therefore, an automatic diagnosis system is desirable. As the crucial pre-processing step for cell pattern recognition, the performance of cell segmentation is crucial. In this paper, a novel adaptive local thresholding approach is proposed to solve the issue. The approach divides cell images into overlapping sub-images and applies adaptive threshold estimator to each of them. The ICPR 2014 HEp-2 cell datasets are employed to assess the segmentation performance of our framework. The results show that the system achieves an average segmentation accuracy of 66.95%, which outperforms the typical thresholding approaches.

AB - The patterns of Human Epithelial type 2 (HEp-2) cell provide useful information for the diagnosis of systemic autoimmune diseases. However, the recognition of cell patterns requires manual annotation by experienced physicians, which is subject to inter-observer variability. Therefore, an automatic diagnosis system is desirable. As the crucial pre-processing step for cell pattern recognition, the performance of cell segmentation is crucial. In this paper, a novel adaptive local thresholding approach is proposed to solve the issue. The approach divides cell images into overlapping sub-images and applies adaptive threshold estimator to each of them. The ICPR 2014 HEp-2 cell datasets are employed to assess the segmentation performance of our framework. The results show that the system achieves an average segmentation accuracy of 66.95%, which outperforms the typical thresholding approaches.

KW - Adaptive thresholding

KW - Cell images

KW - Local thresholding

KW - THRESHOLD estimator

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U2 - 10.1109/SIPROCESS.2016.7888247

DO - 10.1109/SIPROCESS.2016.7888247

M3 - Conference contribution

AN - SCOPUS:85018671595

T3 - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

SP - 174

EP - 178

BT - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

Y2 - 13 August 2016 through 15 August 2016

ER -

Zhou X, Li Y, Shen L. A novel adaptive local thresholding approach for segmentation of HEp-2 cell images. In 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 174-178. 7888247. (2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016). doi: 10.1109/SIPROCESS.2016.7888247

A novel adaptive local thresholding approach for segmentation of HEp-2 cell images

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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