Image segmentation of level set based on maximization of between-class variance and distance constraint function

Authors:
Changxiong Zhou;Zhifeng Hu;Shufen Liu;Ming Cui;Rongqing Xu
Affiliations:
JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China and Department of Electronic Information Engineering, Suzhou Voc ...;JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China and Department of Electronic Information Engineering, Suzhou Voc ...;JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China and Department of Electronic Information Engineering, Suzhou Voc ...;JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China and Department of Electronic Information Engineering, Suzhou Voc ...;College of Optoelectronic Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

In most existing level set models for image segmentation, it is necessary to constantly re-initialize the level set function, or to acquire the gradient flow information of the image to restrict the evolution of the curve. A novel image segmentation model of level set is proposed in the paper, which is based on the maximization of the between-class variance and the distance-based constraint function. In this model, the distance-based constraint function is introduced as the internal energy to ensure that the level set function is always the signed distance function (SDF), so that the constant re-initialization of the level set function during the evolution process is avoided. Meanwhile, the external energy function (between-class variance function) is constructed based on the weighted sum of square of the difference between the average grey levels of the target region and the overall region, the background and the overall region respectively. This function is maximized to ensure that the curve represented by zero level set converges towards the target boundary stably. Experimental results show that the constant re-initialization in traditional models has been eliminated in the proposed model. Furthermore, since region information has been incorporated into the energy function, the model renders good performance in the segmentation of both weak edges images and those with Gaussian noise or impulse noise.