一种基于新的图象力的水平集算法

针对用传统的水平集方法处理心脏MRI（核磁共振图）这类含有强噪音、弱边界等
现象的图像很难取得理想的分割结果的问题，这里将聚类信息和图像ROI 信息相结合，构造基于新的图像力的水平集速度函数，该速度函数有较强的抗噪性能，并且能够处理含有弱边界、低对比度的图像。实验表明该方法具有良好的分割效果。
关键词： 曲线演化；Level Set 方法；MRI；k-均值聚类；图像力

Level Set method Based on New Image Force Cai Huihua Zhou Yongjin Yang wenguo Zhang Jianwei (Nanjing University of Information & Science Technology, Department of Mathematic, Nanjing Jiangsu 210044, China) Abstract: It is hard to get good segmentation result by using the classic Level Set method for Magnetic Resonance Imaging (MRI) which has strong noises and weak edges. To solve these problems, this paper integrates clustering information of image with information of the region of interesting, and constructs a new velocity function based on this new image force. This new velocity function has better antinoise capability and can deal with images which have strong noises, weak edges and low contrast. The experiments showed the effective of this method.
Keywords: Curve evolving; Level Set method; MRI; k-means Clustering; image force