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Segmentation of MR image using local and global region based geodesic model.

Xiuming Li1,2,3, Dongsheng Jiang4,5, Yonghong Shi6,7

  • 1Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. mxiul305@163.com.

Biomedical Engineering Online
|May 15, 2015
PubMed
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This study introduces a novel level set geodesic model for segmenting magnetic resonance (MR) images. The method effectively suppresses intensity inhomogeneity, improving segmentation accuracy for medical imaging applications.

Area of Science:

  • Medical Image Analysis
  • Computational Imaging
  • Biomedical Engineering

Background:

  • Accurate segmentation of magnetic resonance (MR) images is crucial for medical image analysis.
  • Intensity inhomogeneity, noise, and weak boundaries present significant challenges in MR image segmentation.

Purpose of the Study:

  • To develop a novel level set geodesic model for robust MR image segmentation.
  • To address challenges posed by intensity inhomogeneity and weak boundaries in MR images.

Main Methods:

  • A new local and global region-based signed pressure force (SPF) function is proposed.
  • The global SPF is adaptively balanced using local image contrast.
  • A two-phase level set formulation is extended to a multi-phase formulation for brain MR image segmentation.

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Main Results:

  • The proposed method demonstrates robustness and efficiency on synthetic and real MR images.
  • It is computationally efficient and less sensitive to initial contour placement compared to existing methods.
  • Validation on T1-weighted brain MR images shows promising segmentation results.

Conclusions:

  • A novel segmentation model integrating local and global information into the GAC model is presented.
  • The model effectively segments inhomogeneous MR images.
  • It allows for flexible initialization of object contours.