A regularization technique for closed contour segmentation in ultrasound images.

Segmentation of a target object in the form of a closed curve has many potential applications in medical imaging because it provides quantitative information related to the target objext's size and shape. However, ultrasound image segmentation for boundary delineation of the target object is a very difficult task because of its inherent drawbacks, including uncertainty of the segmentation boundary caused by speckle noise, relatively low SNR, and low contrast. Indeed, in automatic ultrasound image segmentation, conventional techniques with standard regularization often fail to reach the desired segmentation in the form of a simple closed curve because of the weakness of edge detector functions in finding the likely target boundary. In this paper, we propose a new regularization model which has the property of encouraging a closed curve by deliberately controlling the curve smoothness. The new model may be combined with various fitting terms to enhance segmentation results. The key features of the proposed model are demonstrated in detail. Numerical simulations and experiments show that the proposed model enhances the segmentation ability for extracting the target boundary as a closed contour.