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Segmentation and Automatic Identification of Vasculature in Coronary Angiograms
Yaofang Liu1, Wenlong Wan2, Xinyue Zhang1
1School of Mathematical Sciences, Ocean University of China, 238 Songling Road, Qingdao, Shandong 266100, China.
Insights
This study introduces an advanced image processing technique to improve coronary angiography analysis. The method enhances visualization and accurately identifies coronary artery structures, aiding in diagnosing coronary heart disease.
Area of Science:
- Medical Imaging
- Cardiovascular Diagnostics
- Image Processing
Background:
- Coronary angiography is crucial for diagnosing coronary heart disease.
- Challenges in coronary artery analysis include complex structures and image noise.
- Accurate vessel segmentation and identification are vital for diagnosis.
Purpose of the Study:
- To develop an effective preprocessing scheme for enhancing coronary angiogram quality.
- To accurately segment coronary vessels using the C-V model.
- To propose an improved adaptive tracking algorithm for automatic vascular skeleton identification.
Main Methods:
- Image preprocessing techniques: block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement.
- Vessel segmentation using the C-V model for contour extraction.
- Development of an improved adaptive tracking algorithm for vascular skeleton identification.
Main Results:
- The preprocessing scheme successfully enhances vascular structures and suppresses background noise.
- The C-V model accurately extracts continuous vessel contours.
- The proposed tracking method demonstrates higher accuracy and robustness than existing methods.
Conclusions:
- The integrated approach significantly improves coronary angiogram quality and analysis.
- Accurate vessel segmentation and skeleton identification are achievable with the proposed methods.
- This technique offers a more robust and accurate tool for coronary heart disease diagnosis.
Abstract:
Coronary angiography is the "gold standard" for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method.
