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3D/2D Model-to-Image Registration for Quantitative Dietary Assessment.

Hsin-Chen Chen1, Wenyan Jia2, Zhaoxin Li3

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, R.O.C ; Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Proceedings of the IEEE ... Annual Northeast Bioengineering Conference. IEEE Northeast Bioengineering Conference
|June 20, 2014
PubMed
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This study introduces a new framework for estimating food volume from a single 2D image using 3D/2D model-to-image registration. The method accurately calculates food volume for health monitoring and dietary management.

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Nutritional Science

Background:

  • Accurate dietary assessment is crucial for health monitoring and management.
  • Image-based methods offer objective quantification of food intake, including volume, nutrients, and calories.
  • Existing methods may lack precision in estimating food volume from single images.

Purpose of the Study:

  • To present a novel framework for estimating food volume from a single 2D image.
  • To develop a robust method utilizing 3D/2D model-to-image registration for dietary assessment.
  • To enhance the accuracy of quantitative dietary analysis through advanced image processing techniques.

Main Methods:

  • Food segmentation from background using Otsu's thresholding and morphological operations.

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  • 3D/2D model-to-image registration to optimize the position, orientation, and scale of a 3D food model.
  • Utilizing a circular plate as a reference object for spatial constraints in the registration process.
  • Incorporating global contour information of the 3D shape model for reliable volume estimation.

    • Main Results:

    • The proposed method effectively estimates food volume from single-view 2D images.
    • Experimental results validated the approach using regularly shaped objects and realistic food models.
    • The integration of a reference object (circular plate) improved the accuracy of the registration and volume estimation.

    Conclusions:

    • The 3D/2D model-to-image registration framework provides an effective solution for image-based food volume estimation.
    • This technique holds potential for improving objective dietary assessment in health monitoring and management.
    • The method's reliance on a single image and a reference object makes it practical for real-world applications.