Automatic prostate boundary detection in ultrasound images using multiresolution deformable models and fuzzy logic

Abstract

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.