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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient

Krit Somkantha1, Nipon Theera-Umpon, Sansanee Auephanwiriyakul

  • 1Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand. krich_cpe@hotmail.com

IEEE Transactions on Bio-Medical Engineering
|November 11, 2010
PubMed
Summary

This study presents a novel edge following technique for accurate boundary detection in noisy images. The method effectively segments various medical images, outperforming traditional contour models.

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Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Image Segmentation

Background:

  • Accurate boundary detection in noisy images remains a significant challenge in medical image analysis.
  • Existing methods often struggle with noise, impacting segmentation accuracy.

Purpose of the Study:

  • To introduce a novel edge following technique for robust boundary detection in noisy images.
  • To evaluate the technique's performance across diverse medical imaging modalities.

Main Methods:

  • The proposed technique utilizes intensity gradient information via a vector image model and texture gradient via an edge map.
  • It employs an edge following approach for object boundary detection.
  • Performance was validated on synthetic and real medical images (ultrasound, MRI, CT).

Main Results:

  • The technique successfully segmented objects in various noisy medical images, including prostates, ventricles, aortas, and knee joints.
  • It demonstrated superior performance compared to established active contour models (ACM) and gradient vector flow snake models.
  • Ground truth was established using expert physician assessments.

Conclusions:

  • The developed edge following technique offers a robust solution for boundary detection in noisy images.
  • It shows significant improvements over classical contour models, particularly in medical image segmentation.
  • The method is applicable to various noisy image types without requiring prior noise characteristic knowledge.