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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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A VARIATIONAL FRAMEWORK FOR PARTIALLY OCCLUDED IMAGE SEGMENTATION USING COARSE TO FINE SHAPE ALIGNMENT AND

Lin Yang1, David J Foran

  • 1Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854.

Proceedings. International Conference on Image Processing
|March 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new variational framework for image segmentation, effectively handling occluded objects by combining top-down and bottom-up information. The method utilizes shape priors and advanced density approximation for robust performance in real-world scenarios.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Partially occluded image segmentation presents a significant challenge in computer vision.
  • Existing methods often struggle with variations in object shape and class.

Purpose of the Study:

  • To propose a novel variational framework for robust image segmentation, specifically addressing partial occlusions.
  • To integrate top-down and bottom-up information for improved segmentation accuracy.
  • To handle intra- and inter-class shape variations effectively.

Main Methods:

  • A variational framework combining top-down and bottom-up image information.
  • Application of shape priors, including shape mode clustering and non-rigid transformation estimation.
  • Semi-parametric density approximation utilizing adaptive meanshift and L(2)E robust estimation for likelihood modeling.

Main Results:

  • Demonstrated good performance on a set of real-world images.
  • Effective handling of partially occluded objects.
  • Successful management of coarse-to-fine variations in shape.

Conclusions:

  • The proposed variational framework offers a robust solution for partially occluded image segmentation.
  • The integration of shape priors and advanced density modeling enhances segmentation accuracy.
  • The algorithm shows promise for practical applications in image analysis.