Digital Processing Techniques for Transmission Electron Microscope Images of Combustion-generated So...
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Digital Processing Techniques for Transmission Electron Microscope Images of Combustion-generated Soot. Bing Hu and Jiangang Lu Department of Civil and Environmental Engineering University of Wisconsin – Madison. Motivation and Background.

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Bing hu and jiangang lu department of civil and environmental engineering

Digital Processing Techniques for Transmission Electron Microscope Images of Combustion-generated Soot

Bing Hu and Jiangang Lu

Department of Civil and Environmental Engineering

University of Wisconsin – Madison


Motivation and background

Motivation and Background

  • Quantified characterization of flame-generated soot is critical for soot research.

  • TEM-based study of soot properties is a reliable approach to quantifying soot size and morphology.

  • Limited to the quality of TEM images, this approach may be facing challenges.


Objective

Objective

  • By applying extensive digital image processing techniques to TEM images of soot particles, images with high qualities in senses of machine detection as well human visual inspection can be achieved.

  • Developed an accurate as well as efficient computational analysis of soot size and morphology based on automatic computer detection.


Typical tem images of soot

Typical TEM Images of Soot

  • Low contrast, noise

  • Pseudo edges caused by electron diffraction


Approach

Approach

  • Enhance contrast by gray level transformation.

  • Reduce noise by low-pass filtering.

  • Eliminate pseudo bright edges by blurring filtering.

  • Segmentation of foreground from background by thresholding.

  • Compensate for imperfect thresholding by morphological processing.

  • Identify objects by morphology processing and segmentation.

  • Computational analysis based on pixel value.


Contrast enhancement

Contrast Enhancement


Noise fines detail removal

Noise/fines detail Removal


Thresholding

Thresholding

  • Global Thresholding

  • Adaptive Local Thresholding


Morphologic processing

Morphologic Processing


Object extraction and measurement

Object Extraction and Measurement

  • Identify objects through extracting connected components.

  • Measure maximum length.

  • Measure projected area.


Summary and conclusions

Summary and Conclusions

  • An economical, accurate, and rapid image processing and analysis approach has been developed for analyzing soot morphology information from the Transmission Electron Microscope images.

  • The techniques involved in this study include gray level transformation, convolution filtering, histogram analysis, thresholding, edge detection, image opening, extraction of connected components, and computational pixel analysis.


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