Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most
strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological
treatments if identified early by monitoring carotid plaque changes. Plaque composition information from
magnetic resonance (MR) carotid images and dynamic characteristics information from 3D ultrasound (US) are
necessary for developing and validating US imaging tools to identify vulnerable carotid plaques. Combining these
images requires nonrigid registration to correct the non-linear miss-alignments caused by relative twisting and
bending in the neck due to different head positions during the two image acquisitions sessions.
The high degree of freedom and large number of parameters associated with existing nonrigid image registration
methods causes several problems including unnatural plaque morphology alteration, computational
complexity, and low reliability. Our approach was to model the normal movement of the neck using a "twisting
and bending model" with only six parameters for nonrigid registration. We evaluated our registration technique
using intra-subject in-vivo 3D US and 3D MR carotid images acquired on the same day. We calculated the Mean
Registration Error (MRE) between the segmented vessel surfaces in the target image and the registered image
using a distance-based error metric after applying our "twisting bending model" based nonrigid registration
algorithm. We achieved an average registration error of 1.33±0.41mm using our nonrigid registration technique.
Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with our
non-rigid registration technique.
In this paper, we propose an automatic model based image segmentation system, where the instantiated model is refined incrementally using the domain knowledge combined by Fuzzy Logic. The Fuzzy Inference System (FIS) combines several different image features, which are used by experts to detect prostates in noisy ultrasound images. We use the Discrete Dynamic Contour (DDC) model because of its favorable performances in both open and closed contour models. The FIS governs the automatic open DDC model initialization and the following incremental growing process on a low-resolution image. At this stage, the initial open contour model grows by tracking the coarse edge details until it closes. The resulting closed contour model is then refined incrementally up to the original image resolution, incorporating finer edge details on to the model. The algorithm developed here is a general tool for object detection in an image analysis system, which employs a flexible framework designed to support multiple decision tools to collaborate in forming a solution. The FIS in our tool retrieves the domain knowledge it needs from the framework, to govern the model refinement process. The proposed algorithm can be used to detect the boundary of any object on an image, if the knowledge of the dominant image features is stored in the system. We have included results of the algorithm successfully applied to several ultrasound images to define the boundary of the prostate.