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A real-time biopsy needle segmentation technique using Hough transform.

Mingyue Ding1, Aaron Fenster

  • 1Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, P.O. Box 5015, London, Ontario N6A 5K8, Canada.

Medical Physics
|August 30, 2003
PubMed
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This study presents a fast Hough Transform method for real-time needle segmentation in medical imaging. The approach achieves high accuracy on affordable computers, enabling faster image-guided procedures.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Real-time needle segmentation and tracking are crucial for image-guided surgery, biopsy, and therapy.
  • The Hough Transform is a robust line-detection technique but computationally intensive for real-time applications.
  • Existing fast implementations of the Hough Transform are insufficient for real-time performance on standard hardware.

Purpose of the Study:

  • To develop a computationally efficient algorithm for real-time needle segmentation using the Hough Transform.
  • To enable real-time needle tracking on affordable computer systems without specialized hardware.
  • To improve the speed and accessibility of image-guided medical procedures.

Main Methods:

  • A novel fast implementation of the Hough Transform utilizing a coarse-fine search strategy.

Related Experiment Videos

  • Determination of optimal image resolution to balance accuracy and computational load.
  • Algorithm validation using ultrasound (US) image sequences from agar phantoms and patient breast biopsies.

    • Main Results:

    • The proposed method achieves needle segmentation in under 33 milliseconds on an affordable PC.
    • Significant reduction in segmentation time, an order of magnitude faster than conventional techniques.
    • High accuracy demonstrated with an angular root-mean-square error of approximately 1 degree and a position root-mean-square error of approximately 0.5 mm.

    Conclusions:

    • The developed fast Hough Transform implementation enables real-time needle segmentation on standard computers.
    • This advancement facilitates more efficient and accessible image-guided interventions.
    • The approach offers a practical solution for improving needle tracking in medical applications.