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A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors
Vikram V Appia1, Balaji Ganapathy, Amer Abufadel
1Georgia Institute of Technology, Atlanta, GA, USA.
Summary
This study introduces a novel region-based segmentation model incorporating shape priors and confidence labels. It effectively excludes irrelevant image regions, improving segmentation accuracy by focusing on informative areas.
Area of Science:
- Computer Vision
- Image Processing
- Medical Imaging
Background:
- Accurate image segmentation is crucial for various applications.
- Traditional methods struggle with regions of low confidence or user-defined interest.
- Existing models may be misled by irrelevant image data.
Purpose of the Study:
- To develop an improved region-based segmentation model using shape priors.
- To incorporate confidence/interest labels to exclude uninformative image regions.
- To enhance segmentation accuracy by mitigating the influence of weak or irrelevant data.
Main Methods:
- A region-based segmentation model with shape priors was developed.
- An auxiliary map indicating low confidence/interest regions was generated during training.
- A parametric model evolved global parameters using an objective energy functional, weighted by confidence labels.
Main Results:
- The model successfully estimates and excludes regions with low confidence or user-defined interest.
- Segmentation accuracy improved, particularly in challenging regions, by prioritizing informative areas.
- Excluding misleading low-confidence regions enhanced overall segmentation quality, even in accurate edge areas.
Conclusions:
- The proposed model offers a robust approach to image segmentation by intelligently handling uninformative regions.
- Incorporating confidence/interest labels significantly improves segmentation performance and reliability.
- This method provides more accurate segmentation of desired objects by focusing on relevant image data.
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