分段常值多相图像分割的变分水平集方法

Variational level set methods for multiphase image segmentation based on piecewise constant

基于变分水平集方法,建立一种具有噪声去除能力的分段常值多相图像分割模型.该模型能在完成噪声去除的同时完成图像分割,从而缩短针对噪声图像的分割时间;其次,基于十进制数和二进制数转换,构造区域划分的特征函数表达式,从而建立一个新的多相图像分割模型;最后把该多相图像分割模型应用于合成图像和医学图像的分割.实验结果表明,与Chan-Vese模型相比,该模型能更快地提取噪声图像中的目标.

A multiphase image segmentation model with the noise removal ability is proposed based on the variational level set mehod.The new model can complete the segmentation and the denoising process simultaneously.Therefore it can reduce the execution time of image segmentation.On the other hand,we construct the characteristic function of the regional division based on the decimal-binary number conversion.Finally,the proposed model is applied to the segmentation of the synthetic images and medical images.The experimental results show that the proposed model can extract the targets from the noise image faster than Chan-Vese model.