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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Orientation histograms as shape priors for left ventricle segmentation using graph cuts.

Dwarikanath Mahapatra1, Ying Sun

  • 1Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576. dmahapatra@gmail.com

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 19, 2011
PubMed
Summary

This study introduces a new graph cut method for segmenting the left ventricle (LV) in cardiac MRI. The technique uses shape priors to improve accuracy in low-contrast images.

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Area of Science:

  • Medical Imaging
  • Cardiovascular Imaging
  • Image Segmentation

Background:

  • Cardiac magnetic resonance imaging (MRI) presents challenges in segmenting the left ventricle (LV) due to poor image contrast.
  • Accurate LV segmentation is crucial for diagnosing and monitoring cardiac conditions.

Purpose of the Study:

  • To develop a novel graph cut framework for robust segmentation of the left ventricle (LV) from dynamic cardiac perfusion images.
  • To leverage shape prior information to overcome contrast limitations in cardiac MRI segmentation.

Main Methods:

  • A graph cut framework incorporating shape priors was proposed for LV segmentation.
  • Shape prior information was derived from a reference LV image.
  • A novel shape penalty was formulated based on orientation angles between pixels and prior shape edge points.
  • The method accounts for cardiac deformation by allowing label changes near the prior shape boundary.

Main Results:

  • The proposed method demonstrated superior performance in segmenting the left ventricle (LV) compared to existing techniques.
  • Experimental results on real patient datasets validated the effectiveness of the shape prior-based graph cut approach.
  • Distinct distributions of orientation angles were observed for points inside and outside the LV, aiding segmentation.

Conclusions:

  • The novel graph cut framework with shape priors effectively addresses the challenge of LV segmentation in low-contrast cardiac MRI.
  • This method offers improved accuracy and robustness for cardiac image analysis.
  • The approach shows significant potential for clinical applications in cardiovascular imaging.