Abstract
Coronary arteries are the vascular structures that supply blood to the heart muscle. Identification of anomalies in the coronary arteries from the 2D slices is a challenging and time consuming process. Segmenting the coronary arteries from the 2D slices and reconstructing into 3D images help in analyzing abnormalities and enable easy diagnosis for medical experts. Hence in this research work, we propose an automated system that extracts coronary arteries from 2D slices of Computed Tomography Angiography (CTA) images using machine learning techniques and reconstruct them into 3D coronary artery tree. Our proposed method extracts statistical features from the arteries and classifies them as coronary and non-coronary arteries using Support Vector Machine (SVM). Classified coronary arteries from 2D slices are reconstructed into 3D coronary artery tree using Maximum Intensity Projection (MIP) algorithm. The performance of our proposed technique is evaluated using Structure Similarity Index Metric (SSIM).
Highlights
Cardiovascular Diseases (CVD) are life threatening diseases in the world which includes high blood pressure, Coronary Heart Disease (CHD), congestive heart failure, stroke and congenital cardiovascular defects
Several imaging techniques are available for diagnosing CHD, Han et al (2008) has proposed Computed Tomography Angiography (CTA) as the best imaging modality
Support vector machine: Support Vector Machine (SVM) is a machine learning technique used for classification
Summary
Cardiovascular Diseases (CVD) are life threatening diseases in the world which includes high blood pressure, Coronary Heart Disease (CHD), congestive heart failure, stroke and congenital cardiovascular defects. Several imaging techniques are available for diagnosing CHD, Han et al (2008) has proposed Computed Tomography Angiography (CTA) as the best imaging modality. CTA provides greater details about soft tissues and blood vessels and produces multiple images for an organ called as slices. Even though it produces greater details, diagnosing diseases from 2D slices is a complex task. The coronary arteries are the vascular structures which supply blood to the heart. Identification of anomalies in the coronary arteries from the 2D slices is very important. We propose an automated system which identifies, segments the coronary arteries from 2D axial slices and reconstructs 3D coronary artery tree for easy diagnosis
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