A modified Frangi’s vesselness measure based on gradient and grayscale values for coronary artery detection

This paper describes a method to extract the cardiac vessels in coronary angiography images. The cardiac vessel extraction technique is a significant process in clinical scenario for cardiac image analysis of Coronary Computed Tomography Angiography (CCTA) datasets. CCTA is a speedy growing non-invasive cardiac imaging modality that provides vital diagnostic information for the cardiac disease diagnosis. Since cardiac vessel extraction for CTA images is a prime issue in computer-aided medical diagnosis, algorithms or systems for vessel detection are always demanded. In order to support computer-aided diagnosis, a modified Frangi’s vesselness measure based on gradient and grayscale measure of the cardiac images is proposed in this work. The experimental result shows that the proposed method can effectively enhance vascular structures and suppress the pseudo vascular structures. It eliminates the background noise and helps in separation of neighboring vessels. The proposed vesselness measure were statistically analyzed by analysis of variance (i.e) one-way ANOVA. The statistical analysis also proves that the proposed vesselness measure based on gradient and grayness values extracts the vessel segments more effectively from the background. Hence the method detects the cardiac vessels more effectively by incorporating the fact that the gradient and grayscale values are comparatively different inside and outside the cardiac vessels. The proposed method has been evaluated on 3D CCTA images and the results are promising.