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An edge extraction technique for noisy images.

K J Cios1, A Sarieh

  • 1University of Toledo, OH.

IEEE Transactions on Bio-Medical Engineering
|May 1, 1990
PubMed
Summary
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This study introduces an unsupervised learning algorithm for edge extraction in noisy images. The method effectively identifies edges in both artificial and real-world medical images, showing promise for image processing applications.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Edge extraction is crucial for image analysis.
  • Noise significantly degrades image quality and complicates edge detection.
  • Existing methods may struggle with high levels of noise.

Purpose of the Study:

  • To develop an algorithm for robust edge extraction from noisy images.
  • To utilize unsupervised learning for adaptive threshold computation.
  • To evaluate the algorithm's performance on synthetic and real-world noisy data.

Main Methods:

  • An unsupervised learning approach for local threshold computation.
  • Pearson's method for mixture density identification to determine thresholds.
  • Application to computer-generated images with artificial noise.

Related Experiment Videos

  • Testing on a real Thallium-201 heart image.

    • Main Results:

    • The algorithm successfully extracts edges from images corrupted with artificial noise.
    • Effective edge detection was demonstrated on a clinical Thallium-201 heart image.
    • The technique shows potential for handling noisy image data.

    Conclusions:

    • The proposed unsupervised learning algorithm is effective for edge extraction in noisy images.
    • Pearson's method aids in robust local threshold computation.
    • The technique offers a viable solution for improving image analysis in the presence of noise.