融合纹理与形状特征的HEp-2细胞分类

doi:10.11999/JEIT161090

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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.

Key words： HEp-2 Local triple pattern Improved Fisher Vector (IFV)

Received: 17 October 2016 Published: 11 May 2017

PACS:

TP391

Fund:

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

Corresponding Authors: ZHANG Dongbo E-mail: zhadonbo@163.com

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	WEN Dengwei
	ZHANG Dongbo
	TANG Hongzhong
	XU Haixia

Cite this article:

WEN Dengwei,ZHANG Dongbo,TANG Hongzhong等. HEp-2 Cell Classification by Fusing Texture and Shape Features[J]. JEIT, 2017, 39(7): 1599-1605.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT161090 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I7/1599

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