HEp-2 Cell Classification by Fusing Texture and Shape Features

WEN Dengwei; ZHANG Dongbo; TANG Hongzhong; XU Haixia

doi:10.11999/JEIT161090

Volume 39 Issue 7

Jul. 2017

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WEN Dengwei, ZHANG Dongbo, TANG Hongzhong, XU Haixia. HEp-2 Cell Classification by Fusing Texture and Shape Features[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090

Citation:

WEN Dengwei, ZHANG Dongbo, TANG Hongzhong, XU Haixia. HEp-2 Cell Classification by Fusing Texture and Shape Features[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090

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HEp-2 Cell Classification by Fusing Texture and Shape Features

doi: 10.11999/JEIT161090

1.
(Institute of Information Engineering, Xiangtan University, Xiangtan 411105, China)
2.

Funds:

The National Natural Science Foundation of China (61602397), The Natural Science Foundation of Hunan Province (2017JJ2251, 2017JJ3315), The Key Discipline Construction Project of Hunan Province

Received Date: 2016-10-17
Rev Recd Date: 2017-04-06
Publish Date: 2017-07-19

Abstract

Abstract

Indirect Immuno Fluorescence (IIF) HEp-2 cell image analysis is an important basis for the diagnosis of autoimmune diseases. However, due to the great changes in the class and the similarity between the categories, HEp-2 cell staining pattern classification is a difficult problem. This paper presents an effective classification method based on the texture and shape information, learning from the principle of CLBP, a descriptor extracting texture information is proposed to describe the Complete information of the Local Triple Pattern (CLTP). Moreover, using Improved Fisher Vector (IFV) model and Rootsift feature, the shape information can be described. Through the combination of the texture and shape information, an SVM classifier is finally trained and an experiment is conducted in ICPR 2012 and ICIP 2013 data sets. Experiment results show that this method is superior over other methods in the cell level test and present competitive performance.
- HEp-2,
- Local triple pattern,
- Improved Fisher Vector (IFV)

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References(24)

References

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