An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation
Chunhua Dong1, Xiangyan Zeng1, Lanfen Lin2
1Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA, USA.
Journal of Healthcare Engineering
|December 5, 2017
Summary
This study introduces a novel Bayes random walk (RW) framework for accurate volumetric medical image segmentation. The method leverages prior knowledge from segmented slices to improve organ segmentation, outperforming conventional RW techniques.
Area of Science:
- Medical Imaging
- Computer Vision
- Biomedical Engineering
Background:
- The Random Walk (RW) method is common for volumetric medical image segmentation but suffers from large graph sizes and inaccurate segmentation due to poor seed point selection.
- Classical RW algorithms do not effectively utilize organ intensity and shape information.
Purpose of the Study:
- To develop a prior knowledge-based Bayes random walk framework for improved volumetric medical image segmentation.
- To enhance segmentation accuracy by utilizing information from previously segmented slices.
Main Methods:
- A slice-by-slice segmentation approach using a Bayes random walk framework.
- Employing prior shape and intensity knowledge from adjacent segmented slices to guide segmentation.
- Dynamically updating seed points using the narrow band threshold (NBT) method and a Gaussian process-based organ model.
Main Results:
- The proposed Bayes RW framework significantly improved liver segmentation accuracy compared to conventional RW and state-of-the-art interactive methods (p < 0.001).
- The method demonstrated effective utilization of prior knowledge for automated, high-quality image segmentation.
Conclusions:
- The prior knowledge-based Bayes random walk framework offers a more accurate and robust solution for volumetric medical image segmentation.
- This approach addresses limitations of traditional RW methods by incorporating anatomical and intensity priors for improved organ segmentation.
