Vessel filtering and segmentation of coronary CT angiographic images
Yan Huang1,2, Jinzhu Yang3,4, Qi Sun1,2
1Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
Insights
This study introduces an automated method for coronary artery segmentation in CT angiography (CTA) images. The approach accurately segments vessels with low computational cost, proving effective for clinical applications.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Diagnosis
- Image Processing
Background:
- Coronary artery segmentation in coronary computed tomography angiography (CTA) is vital for diagnosing cardiovascular diseases.
- Automated segmentation is challenging due to image complexity and intricate coronary structures.
Purpose of the Study:
- To develop an accurate and efficient automatic method for coronary artery segmentation in CTA images.
- To address the limitations of existing segmentation techniques.
Main Methods:
- A novel method utilizing a symmetrical radiation filter (SRF) and D-means clustering is proposed.
- SRF filters suspicious vessel tissue based on gradient features on vascular boundaries.
- D-means clustering is integrated to mitigate noise in CTA images.
Main Results:
- The method was evaluated on 210 CTA datasets against manual segmentations.
- Achieved high performance metrics including Jaccard and Dice coefficients.
- Demonstrated superior performance compared to related methods on public datasets.
Conclusions:
- The proposed method provides complete, robust, and accurate coronary artery segmentation.
- It operates with low computational cost and does not require extensive training data.
- The technique is suitable for clinical applications in CTA image analysis.
Purpose:
Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images.
Method:
In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images.
Results:
The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods.
Conclusion:
The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.
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