Automatic Detection of Immunogold Particles from Electron Microscopy Images

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• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9164
• 期：1
• 页码：377-384
• 全文大小：1,806 KB
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• 作者单位：Ricardo Gamelas Sousa (15) (16)
  Tiago Esteves (15) (16) (18)
  Sara Rocha (20)
  Francisco Figueiredo (15) (17)
  Pedro Quelhas (15) (16) (21)
  Luís M. Silva (15) (16) (19)
  
  15. Instituto de Investiga??o e Inova??o em Saúde (i3S), Porto, Portugal
  16. Instituto de Engenharia Biomédica (INEB), Porto, Portugal
  18. Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
  20. Centro de Biotecnologia dos A?ores (CBA), Universidade dos A?ores, A?ores, Portugal
  17. Instituto de Biologia Molecular e Celular (IBMC), Porto, Portugal
  21. Metaio GmbH, Munich, Germany
  19. Dep. de Matemática, Universidade de Aveiro, Aveiro, Portugal
  
• 丛书名：Image Analysis and Recognition
• ISBN：978-3-319-20801-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Immunogold particle detection is a time-consuming task where a single image containing almost a thousand particles can take several hours to annotate. In this work we present a framework for the automatic detection of immunogold particles that can leverage significantly the burden of this manual task. Our proposal applies a Laplacian of Gaussian (LoG) filter to provide its detection estimates to a Stacked Denoising Autoencoder (SdA). This learning model endowed with the capability to extract higher order features provides a robust performance to our framework. For the validation of our framework, a new dataset was created. Based on our work, we determined that solely the LoG detector attained more than 74.1?% of accuracy and, when combined with a SdA the accuracy is improved by at most 11.4?%.