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Related Experiment Video

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Temporal segmentation of lung region MR image sequences using hough transform.

Renato Seiji Tavares1, Andre Kubagawa Sato, Marcos de Sales Guerra Tsuzuki

  • 1Escola Politécnica, São Paulo University, Brazil.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

This study presents an automated method for lung segmentation in MR images, crucial for 3D reconstruction. The novel approach improves accuracy despite image quality variations and breathing motion.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Lung segmentation is vital for 3D reconstruction and registration.
  • Current segmentation methods are often interactive and struggle with variable MR image quality.
  • Breathing motion introduces challenges in accurately segmenting lung structures.

Purpose of the Study:

  • To develop an automated and robust lung segmentation method for MR images.
  • To improve the accuracy of lung segmentation, particularly in the presence of image quality variations and respiratory motion.
  • To facilitate precise 3D reconstruction and registration of the lung.

Main Methods:

  • A two-step segmentation process involving mask creation and modified Hough transform.
  • Utilizing 2D image processing, edge detection, and Hough transform to obtain respiratory patterns and estimate temporal positions.
  • Leveraging temporal coherence in image sequences to determine lung silhouettes even with unclear edges.

Main Results:

  • Successfully isolated the diaphragmatic surface automatically and robustly.
  • Achieved accurate lung segmentation across temporal sequences, overcoming challenges of obscure edges and image quality variations.
  • Demonstrated the efficacy of the modified Hough transform in handling mask shape variations.

Conclusions:

  • The developed automated segmentation method is effective for lung MR images.
  • This technique enhances the accuracy of lung segmentation, supporting reliable 3D reconstruction and registration.
  • The approach offers a robust solution for segmenting lungs despite common imaging artifacts and physiological motion.