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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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[Segmental active contour model combining regional information].

Xin Ran1, Feihu Qi

  • 1Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|January 19, 2007
PubMed
Summary

This study introduces a new segmental active contour model for accurate image segmentation. The model uses hierarchical deformation and region statistics for robust object boundary detection, even for concave shapes.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Context:

  • Image segmentation is crucial for analyzing visual data.
  • Traditional active contour models struggle with complex object boundaries and local minima.
  • Integrating region information offers a promising approach to enhance segmentation accuracy.

Purpose:

  • To propose a novel segmental active contour model that integrates region information for improved image segmentation.
  • To develop a two-stage deformation strategy for accurate object boundary detection.
  • To enhance robustness against local minima and enable segmentation of concave objects.

Summary:

  • A hierarchical segmental active contour model is presented, employing affine transformations for initial contour deformation.
  • A secondary stage refines the segmentation using local region statistics to redefine external energy for precise boundary fitting.
  • Novel methods for computing internal and external energies reduce algorithmic complexity.

Impact:

  • The proposed model demonstrates robustness against local minima, a common challenge in active contour methods.
  • Experimental results confirm its effectiveness in accurately segmenting complex and concave objects.
  • This work advances image segmentation techniques, offering a more reliable tool for computer vision applications.