2D ultrasound image segmentation using graph cuts and local image features

Ultrasound imaging is a popular imaging modality due to a number of favorable properties of this modality. However, the poor quality of ultrasound images makes them a bad choice for segmentation algorithms. In this paper, we present a semi-automatic algorithm for organ segmentation in ultrasound images, by posing it as an energy minimization problem via appropriate definition of energy terms. We use graph-cuts as our optimization algorithm and employ a fuzzy inference system (FIS) to further refine the optimization process. This refinement is achieved by using the FIS to incorporate domain knowledge in order to provide additional constraints. We show that by integrating domain knowledge via FIS, the accuracy is improved significantly so that further manual refinement of object boundary is often unnecessary. Our algorithm was applied to detect prostate and carotid artery boundaries in clinical ultrasound images and shows the success of the proposed approach.