Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35 IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in image-guided coronary interventions.