Coronary artery extraction and analysis for detection of soft plaques in MDCT images

In this paper we aim to develop a computationally-efficient image-segmentation procedure for detection and quantification of soft plaques in coronary arteries from multidetector CT images. The proposed method consists of three steps: extraction of the arterial lumen centerline, segmentation of the lumen and arterial wall based on a locally-adaptive mixture-model using the expectation- maximization algorithm, and detection of soft plaques based on effective cross-sectional areas of the lumen and of the wall. Preliminary results using clinical acquisitions are presented to demonstrate the effectiveness of the proposed method.