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Active appearance pyramids for object parametrisation and fitting.

Qiang Zhang1, Abhir Bhalerao1, Edward Dickenson2

  • 1Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.

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|April 15, 2016
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Summary
This summary is machine-generated.

We introduce the Active Appearance Pyramid (AAP), a novel part-based model for medical image analysis. This model improves object representation and fitting accuracy, outperforming existing methods in segmentation and reconstruction tasks.

Keywords:
Active appearance modelActive appearance pyramidLumbar spinal stenosisPart-based model

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Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Biomedical Engineering

Background:

  • Object class representation is a critical challenge in medical image analysis.
  • Existing models like Active Shape Models (ASMs) and Active Appearance Models (AAMs) have limitations in specificity and distinctiveness.

Purpose of the Study:

  • To propose a novel part-based parametric appearance model, the Active Appearance Pyramid (AAP).
  • To enhance spatial specificity and distinctiveness in object representation using multi-scale Local Feature Pyramids (LFPs).
  • To improve fitting accuracy and efficiency in medical image analysis tasks.

Main Methods:

  • Developed a part-based parametric appearance model (AAP) utilizing multi-scale Local Feature Pyramids (LFPs).
  • Implemented a two-step iterative fitting strategy: local landmark searching and shape regularization.
  • Utilized a weighted Lucas and Kanade method for simultaneous local feature searching and appearance fitting.
  • Derived a shape regularizer for maximum likelihood shape calculation with a closed-form solution.

Main Results:

  • The AAP demonstrated significant improvements in convergence range, robustness to local minima, and segmentation precision compared to CLMs, ASMs, and AAMs.
  • Achieved superior performance in appearance reconstruction compared to AAMs.
  • Validated on 2D MRI scans for lumbar spinal stenosis (LSS) and 3D CT volumes of hip joints, showing higher segmentation and reconstruction precision.
  • Exhibited significant efficiency gains: reduced memory usage by half, less than 10% training time, and 15% testing time compared to AAMs.

Conclusions:

  • The Active Appearance Pyramid (AAP) offers a robust and efficient approach for object class representation in medical image analysis.
  • AAP significantly outperforms existing models in segmentation, reconstruction, and efficiency.
  • The part-based approach with multi-scale LFPs provides superior spatial specificity and distinctiveness for modeling anatomical variability.