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Related Concept Videos

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Fischer Projections

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The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as conformers.
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Related Experiment Video

Updated: Jul 8, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Detection of edges from projections.

N Srinivasa1, K R Ramakrishnan, K Rajgopal

  • 1Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore.

IEEE Transactions on Medical Imaging
|January 1, 1992
PubMed
Summary

This study introduces a novel method for edge detection directly from projection data in computerized tomography (CT). This approach simplifies processing and enhances efficiency for object characterization in medical imaging.

Area of Science:

  • Medical imaging
  • Computerized tomography
  • Image processing

Background:

  • Computerized tomography (CT) applications often require detecting and characterizing objects within cross-sections.
  • Edge detection from projection data is crucial for obtaining this information.
  • Current methods may involve complex postprocessing of reconstructed images.

Purpose of the Study:

  • To address the challenge of detecting edges directly from projection data in CT.
  • To demonstrate that linear edge detection operators used on images can be applied to projection data.
  • To reduce computational load and avoid postprocessing complexities of reconstructed images.

Main Methods:

  • Utilizing a convolution backprojection operation for direct edge detection.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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  • Applying linear edge detection operators, such as the Marr-Hildreth operator, to projection data.
  • Modifying the filtering function by incorporating the Radon transform of the Laplacian of a 2-D Gaussian function with the reconstruction filter.

    • Main Results:

    • The proposed method enables direct edge detection from projection data, bypassing image reconstruction.
    • Simulation results validate the efficacy of the direct edge detection technique.
    • A comparison shows comparable or improved edge detection compared to methods using reconstructed images.

    Conclusions:

    • Direct edge detection from projection data in CT is feasible and computationally advantageous.
    • This method simplifies the workflow for object detection and characterization in CT.
    • The approach offers a more efficient alternative to traditional postprocessing of reconstructed images.