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Pattern Recognition Software and Techniques for Biological Image Analysis

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  • Lior Shamir
  • John D Delaney
  • Nikita Orlov
  • D Mark Eckley
  • Ilya G Goldberg

Abstract

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

Suggested Citation

  • Lior Shamir & John D Delaney & Nikita Orlov & D Mark Eckley & Ilya G Goldberg, 2010. "Pattern Recognition Software and Techniques for Biological Image Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-10, November.
    • Handle: RePEc:plo:pcbi00:1000974
    DOI: 10.1371/journal.pcbi.1000974
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    References listed on IDEAS

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    1. Asa Ben-Hur & Cheng Soon Ong & Sören Sonnenburg & Bernhard Schölkopf & Gunnar Rätsch, 2008. "Support Vector Machines and Kernels for Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    2. Adi L Tarca & Vincent J Carey & Xue-wen Chen & Roberto Romero & Sorin Drăghici, 2007. "Machine Learning and Its Applications to Biology," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-11, June.
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    1. Assaf Zaritsky & Sari Natan & Judith Horev & Inbal Hecht & Lior Wolf & Eshel Ben-Jacob & Ilan Tsarfaty, 2011. "Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-10, November.
    2. Philipp Mergenthaler & Santosh Hariharan & James M Pemberton & Corey Lourenco & Linda Z Penn & David W Andrews, 2021. "Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-33, February.
    3. Louis-François Handfield & Yolanda T Chong & Jibril Simmons & Brenda J Andrews & Alan M Moses, 2013. "Unsupervised Clustering of Subcellular Protein Expression Patterns in High-Throughput Microscopy Images Reveals Protein Complexes and Functional Relationships between Proteins," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-19, June.
    4. Abderrahim Ayad & Saad Bakkali, 2019. "Fractal Assessment of the Disturbances of Phosphate Series Using Lacunarity and Succolarity Analysis on Geoelectrical Images (Sidi Chennane, Morocco)," Complexity, Hindawi, vol. 2019, pages 1-12, July.

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