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Segmentation of stochastic images with a stochastic random walker method.
1School of Engineering and Science, Jacobs University Bremen, Bremen, Germany. t.paetz@jacobs-university.de
Summary
This study extends random walker segmentation for images with uncertain pixel values. The new method quantifies uncertainty
Area of Science:
- Computer Vision
- Image Processing
- Computational Mathematics
Background:
- Image segmentation is crucial for analyzing medical and scientific images.
- Traditional methods assume precise pixel values, neglecting noise and measurement errors.
- Random walker segmentation offers a powerful graph-based approach but is sensitive to gray-value uncertainty.
Purpose of the Study:
- To extend random walker segmentation to handle images with uncertain gray values.
- To quantify the impact of gray-value uncertainty on segmentation results.
- To provide a reliability estimate for image segmentation outcomes.
Main Methods:
- Introduced the concept of stochastic images where pixel values are treated as random variables.
- Developed stochastic graph weights for random walker segmentation.
- Employed generalized polynomial chaos for discretizing stochastic partial differential equations (PDEs).
Main Results:
- The extended algorithm successfully segments images with uncertain gray values.
- It identifies regions where segmentation is highly influenced by pixel value uncertainty.
- Provides a reliability estimate and the probability density function of the segmented object volume.
Conclusions:
- The proposed method enhances random walker segmentation for real-world imaging scenarios with inherent uncertainty.
- It offers valuable insights into segmentation reliability and object volume estimation.
- This approach advances the robustness of image segmentation techniques in the presence of noise and artifacts.
