Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and aco...Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and acoustic shadow are present A novel approach for hard plaque recognition and media-adventitia border detection of IVUS images is presented in this paper. The IVUS images were first enhanced by a spatial-frequency domain filter that was constructed by the directional filter and histogram equalization. Then, the hard plaque was recognized based on the intensity variation within different regions that were obtained using the k-means algorithm. In the next step, a cost matrix representing the probability of the media-adventitia border was generated by combining image gradient, plaque location and image intensity. A heuristic graph-searching was applied to find the media-adventitia border from the cost matrix.Experiment results showed that the accuracy of hard plaque recognition and media-adventitia border detection was 89.94% and 95.57%, respectively. In conclusion,using hard plaques recognition could improve media-adventitia border detection in IVUS images.展开更多
文摘Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and acoustic shadow are present A novel approach for hard plaque recognition and media-adventitia border detection of IVUS images is presented in this paper. The IVUS images were first enhanced by a spatial-frequency domain filter that was constructed by the directional filter and histogram equalization. Then, the hard plaque was recognized based on the intensity variation within different regions that were obtained using the k-means algorithm. In the next step, a cost matrix representing the probability of the media-adventitia border was generated by combining image gradient, plaque location and image intensity. A heuristic graph-searching was applied to find the media-adventitia border from the cost matrix.Experiment results showed that the accuracy of hard plaque recognition and media-adventitia border detection was 89.94% and 95.57%, respectively. In conclusion,using hard plaques recognition could improve media-adventitia border detection in IVUS images.
