Calcified coronary plaques detection in CTA based-on automatic scale selection and fuzzy C means

Coronary artery diseases are the most common type of heart disease. Early detection and quantification of coronary plaques is therefore of high interest. CTA has rapidly emerged, and is nowadays widely used in clinical practice. A calcification detection and quantification method is proposed, which can detect the calcium plaque and quantify the stenosis of coronary artery in CT images. Firstly, the responses of the DoG operator in the center on each slice of the artery are obtained to estimate the diameter of the vessel. Then, the slice is cropped according to the feature size. Secondly, the Fuzzy C-means is applied to classify the cropped image. Finally, the calcium plaques are extracted with their intensity and the stenosis of the artery caused by the calcified plaque is quantified according to the estimated cross-section area of the vessel. Experiments show that the proposed method can detect and quantify the calcium plaques accurately.