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Related Experiment Video

Updated: Jul 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

Optimal edge detection in two-dimensional images.

R J Qian1, T S Huang

  • 1Coordinated Sci. Lab., Illinois Univ., Urbana, IL.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced edge detection method using a curve-segment functional guided by Laplacian-of-Gaussian zero-crossings. The new approach enhances signal-to-noise ratio and edge localization accuracy for 2-D images.

Related Experiment Videos

Last Updated: Jul 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Mathematics

Background:

  • Edge detection is crucial for image analysis.
  • Traditional methods like Laplacian-of-Gaussian (LOG) have limitations in accuracy and noise handling.
  • Scale-space properties are important for multi-resolution image analysis.

Purpose of the Study:

  • To present a novel edge detection scheme for two-dimensional (2-D) images.
  • To improve upon existing edge detection methods by enhancing accuracy and noise robustness.
  • To leverage the properties of the Laplacian-of-Gaussian (LOG) for precise edge localization.

Main Methods:

  • A curve-segment-based detection functional is proposed.
  • The functional is guided by the zero-crossing contours of the LOG operator.
  • The method operates within a scale-space framework.

Main Results:

  • The proposed detection functional achieves optimal signal-to-noise ratio (SNR).
  • High edge localization accuracy is demonstrated.
  • The method preserves the unique scaling property of the LOG operator.

Conclusions:

  • The new edge detection scheme offers superior performance in terms of SNR and localization.
  • This approach provides a robust method for identifying true edge locations in 2-D images.
  • The preservation of LOG's scaling property enhances its utility in multi-scale image analysis.