Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?

Joint Authors

Shen, Hong-Bin
Yang, Fan
Xu, Ying-Ying

Source

The Scientific World Journal

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-06-23

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Medicine
Information Technology and Computer Science

Abstract EN

Human protein subcellular location prediction can provide critical knowledge for understanding a protein’s function.

Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed.

In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples.

We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature.

According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor.

The combination of these two novel local pattern features and the conventional global texture features is also studied.

The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

American Psychological Association (APA)

Yang, Fan& Xu, Ying-Ying& Shen, Hong-Bin. 2014. Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-1049602

Modern Language Association (MLA)

Yang, Fan…[et al.]. Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?. The Scientific World Journal No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-1049602

American Medical Association (AMA)

Yang, Fan& Xu, Ying-Ying& Shen, Hong-Bin. Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-1049602

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1049602