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Multiple abdominal organ segmentation: an atlas-based fuzzy connectedness approach
1Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. zhouyongxin@tsinghua.edu.cn
Summary
This study introduces an automated method for segmenting abdominal organs using a presegmented atlas within the fuzzy connectedness framework. The approach adaptively estimates parameters and refines segmentation for improved accuracy in medical imaging.
Area of Science:
- Medical Imaging
- Image Analysis
- Computational Anatomy
Background:
- Organ segmentation is crucial for medical image analysis.
- Existing methods often require manual intervention or struggle with overlapping intensities.
Purpose of the Study:
- To develop an automated atlas-based organ segmentation method for abdominal CT and MRI.
- To adaptively estimate fuzzy connectedness (FC) parameters and refine segmentation for improved accuracy.
Main Methods:
- Incorporation of a presegmented atlas into the fuzzy connectedness (FC) framework.
- Automatic and adaptive estimation of FC parameters (organ intensity features, seeds, threshold).
- Shape modification using Euclidean distance and watershed segmentation to handle intensity overlap.
Main Results:
- The atlas-based method successfully segmented abdominal organs in CT and MRI images.
- The adaptive parameter estimation and shape modification improved segmentation accuracy.
- The method demonstrated validity across different image modalities.
Conclusions:
- The proposed atlas-based fuzzy connectedness method provides a valid approach for automatic abdominal organ segmentation.
- Adaptive parameter estimation and shape refinement are key to overcoming segmentation challenges.
- This technique is applicable to various medical imaging modalities.
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