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Mining textural knowledge in biological images: Applications, methods and trends.

Santa Di Cataldo1, Elisa Ficarra1

  • 1Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, Torino 10129, Italy.

Computational and Structural Biotechnology Journal
|December 21, 2016
PubMed
Summary

Automated texture analysis in bioimaging is crucial for identifying diseases like cancer and understanding biological processes. This review covers key methods for analyzing textures in microscopy images of cells and tissues.

Keywords:
BioimagingDeep learningFeature encodingTextural analysisTextural features extractionTexture classification

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Area of Science:

  • Biomedical Imaging
  • Computer Vision
  • Pattern Recognition

Background:

  • Texture analysis is vital in bioimaging for distinguishing cells, tissues, and molecular interactions.
  • Automated analysis aids in diagnosing diseases like cancer and autoimmune disorders, and studying physiological processes.

Approach:

  • This paper critically reviews automated texture analysis methods for biological images.
  • Focuses on feature extraction and encoding techniques applicable to microscopy data.

Key Points:

  • Texture analysis helps detect pathologies and differentiate biological structures.
  • Methods are essential for applications in cancer detection, disease diagnosis, and physiological studies.

Conclusions:

  • The review provides an overview of current state-of-the-art techniques in bioimaging texture analysis.
  • It also highlights emerging trends and future research directions in this field.