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Updated: Mar 2, 2026
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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
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SU-E-J-110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the
Medical Physics
|May 19, 2017
Summary
This study introduces Jensen-Renyi (JR) divergence for robust joint segmentation and registration in radiotherapy imaging. The novel approach significantly outperforms traditional methods in noisy, multi-modality environments.
Area of Science:
- Medical Imaging
- Radiotherapy
- Image Analysis
Background:
- Multimodality image-guided and adaptive radiotherapy require robust segmentation and registration.
- Existing algorithms are often sensitive to noise, limiting their effectiveness.
Purpose of the Study:
- To present a novel joint segmentation/registration framework for radiotherapy.
- To improve noise robustness in multimodality imaging using Jensen-Renyi (JR) divergence.
Main Methods:
- Developed a level set active contour model utilizing Jensen-Renyi (JR) divergence.
- Applied the model for joint segmentation and registration in a multi-modality imaging space.
Main Results:
- JR divergence demonstrated superior noise robustness compared to mutual information (MI) and other entropy-based metrics.
- The MI metric failed at approximately 2/3 the noise power level where JR divergence remained effective.
Conclusions:
- JR divergence is a valuable metric for joint segmentation/registration of multimodality images, outperforming entropy-based metrics.
- The algorithm's adaptability allows for incorporation of non-intensity based images, enabling broader applications in texture analysis.
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