Abstract
Level set methods are effective for image segmentation problems. However, the methods suffer from limitations such as slow convergence and leaking problems. As such, over the past two decades, the original level set method has been evolved in many directions, including integration of prior shape models into the segmentation framework. In this paper, we introduce a new prior shape model for level set segmentation. With a shape model represented implicitly by a signed distance function, we incorporate a local shape parameter to the shape model. This parameter helps to regulate the model fitting process. Based on this local parameter of the shape model, we define a shape energy to drive the level set evolution for image segmentation. The shape energy is coupled with a Gaussian kernel, which acts as a weight distribution on the shape model. This Gaussian effect not only allows evolution of level set to deform around the shape model, but also provides a smoothing effect along the edges. Our approach presents a new dimension to extract local shape parameter directly from the shape model, which is different from previous work that focused on an indirect manner of feature extractions. Experimental results on synthetic, optical and MR images demonstrate the feasibility of this new shape model and shape energy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.