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Many local pattern texture features: which is better for image-based multilabel human protein subcellular

Fan Yang1, Ying-Ying Xu2, Hong-Bin Shen2

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China ; Key Laboratory of Optic-Electronic and Communication, Jiangxi Science & Technology Normal University, Nanchang 330013, China ; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.

Thescientificworldjournal
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Summary

New texture features improve automated protein subcellular location prediction from microscopy images. Completed local binary pattern and local tetra pattern are more effective than standard local binary patterns for classifying human protein images.

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Cell Biology

Background:

  • Accurate protein subcellular location is crucial for understanding protein function.
  • Advancements in digital microscopy necessitate automated methods for protein location classification.
  • Existing methods may not fully capture the complexity of multilabel subcellular image data.

Purpose of the Study:

  • To identify more representative image features for multilabel protein subcellular location classification.
  • To evaluate the discriminative power of novel local texture features (completed local binary pattern, local tetra pattern) against standard local binary patterns.
  • To assess the benefit of combining local and global texture features for improved classification accuracy.

Main Methods:

  • Preparation of a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas.
  • Testing and comparison of completed local binary pattern (CLBP), local tetra pattern (LTP), and standard local binary pattern (LBP) features.
  • Development and evaluation of binary relevance multilabel machine learning models using different feature sets.

Main Results:

  • CLBP and LTP demonstrated superior discriminative ability for IHC images compared to LBP.
  • Combining CLBP and LTP with conventional global texture features significantly enhanced classification performance.
  • The final binary relevance model trained on the combined feature space showed improved accuracy.

Conclusions:

  • Novel local texture features (CLBP, LTP) are more effective for protein subcellular location classification than traditional LBP.
  • Feature fusion, combining local and global texture descriptors, improves classification accuracy.
  • Automated analysis of IHC images using advanced texture features holds promise for advancing protein function studies.