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Image segmentation based on fuzzy connectedness using dynamic weights
Amol S Pednekar1, Ioannis A Kakadiaris
1MR Clinical Science group, Philips Medical Systems North America, Bothell, WA 98021, USA. amol.pednekar@philips.com
Summary
Fuzzy connectedness image segmentation is enhanced with dynamic weights (DyW) to improve accuracy on complex medical images. This method automatically adjusts parameters, outperforming fixed-weight approaches for better segmentation results.
Area of Science:
- Medical image analysis
- Computer vision
- Artificial intelligence
Background:
- Traditional medical image segmentation struggles with fuzzy image characteristics.
- Fuzzy connectedness segmentation captures object proximity and intensity homogeneity.
- Existing methods use fixed weights, requiring extensive user tuning.
Purpose of the Study:
- To introduce a novel fuzzy connectedness segmentation method with dynamic weights (DyW).
- To improve segmentation accuracy and reduce user intervention in medical imaging.
- To address the variability of optimal weight parameters in applications like cardiac MRI.
Main Methods:
- Developed fuzzy connectedness using dynamic weights (DyW) incorporating directional sensitivity.
- Dynamically adjusted linear weights based on image features and homogeneity.
- Applied DyW to segment phantom, MRI, CT, and infrared datasets.
Main Results:
- DyW achieved consistent accuracy exceeding 99.15% across various image complexities.
- The method demonstrated robustness against contrast variations, noise, and bias fields.
- DyW outperformed two traditional fuzzy connectedness formulations.
Conclusions:
- Dynamic weight adjustment in fuzzy connectedness segmentation significantly enhances accuracy.
- DyW offers an automated solution for complex medical image segmentation, reducing manual effort.
- The method shows broad applicability across different imaging modalities and challenging datasets.