Biomedical Image AnalysisNo Access

Somatic Cells Recognition by Application of Gabor Feature-Based (2D)²PCA

Xiaojing Gao

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, P. R. China

E-mail Address: gaoxj4587@imau.edu.cn

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Heru Xue

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, P. R. China

E-mail Address: xuehr@imau.edu.cn

Corresponding author.

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Xin Pan

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, P. R. China

E-mail Address: panx@imau.edu.cn

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Xinhua Jiang

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, P. R. China

E-mail Address: jiangxh@imau.edu.cn

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Yanqing Zhou

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, P. R. China

E-mail Address: 974864134@qq.com

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, and

Xiaoling Luo

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, P. R. China

E-mail Address: luoxl@imau.edu.cn

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https://doi.org/10.1142/S0218001417570099Cited by:5 (Source: Crossref)

Abstract

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)²PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)²PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Keywords:

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