A DEFORMABLE TEMPLATE MODEL WITH FEATURE TRACKING FOR AUTOMATED IVUS SEGMENTATION

Intravascular Ultrasound (IVUS) has been established as a useful tool for diagnosis of coronary heart disease (CHD). Recent developments have opened the possibility of using IVUS to create a 3D map from which preventative prediction of CHD can be attempted. Segmentation of IVUS images is an important step in this process. However reliable automated segmentation has been elusive, in part because of the variety of image features that are invariably present in the image that distract from the main segmentation objectives. Active contour models (ACM)s have been used successfully for automated segmentation of IVUS images. However, the accuracy of the segmentation is still not adequate for clinical use. Here we describe a new approach of a constrained deformable template model (DTM) that improves on the standard ACM algorithm by (1) detecting other distracting image features (2) using tracking algorithms to get a better estimate of the positions of these features (3) including the knowledge of these positions to eliminate distortions in the ACM due to these features. In addition, semantic constraints are inbuilt into the DTM so that computational time is not wasted in improbable segmentation results. Our results show that this is a promising approach to achieving fully automated segmentation with accuracy comparable to manual segmentation.