基于高斯混合模型的活动轮廓模型脑MRI分割

传统的活动轮廓模型用于图像分割往往基于目标的边界信息,在图像含有强噪音或目标具有弱边界时很难得到真实解.引入高斯混合模型构造新的约束项,在新的约束项作用下模型可以减少噪音的影响,并防止从弱边界泄漏.高斯混合模型求解通常使用Expectation-maximization(EM)算法,该算法是局部优化算法,且对初值敏感.因此引入粒子群算法,并提出一种改进的算法,利用该算法的全局优化性求解高斯混合模型的参数,以提高参数精度.对脑核磁共振图像(MRI)分割实验表明该模型具有较好的分割效果.

Brain MRI Segmentation Using the Active Contours Based on Gaussian Mixture Models

Many neuroanatomy studies rely on brain tissue segmentations of magnetic resonance images.In order to segment these images,many active contour methods have been presented.But the traditional active contour method only uses the information of the edge,when it segment magnetic resonance images with strong noise or weak edges,which is popular in medical images,so it is difficult to get the true edge.In this paper the Gaussian mixture model is used to make a new sanction.With this sanction the model can reduce the effect of the noise and prevent the curve over the edge.The expectation-maximization(EM)method is the popular method to solve the Gaussian mixture model,but it is a local optimizer method and is sensitive to the initial value.The global optimization characteristic of the particle swarm optimizer method,which is based on a metaphor of social interaction,is used to solve this problem.The classical particle swarm optimizer method is sensitive to the initial location.In order to overcome this problem,Powell method and new corrupt method are used to adapt the particle swarm optimizer method and with the new adapted particle swarm optimizer method the Gaussian mixture model can get global best results.Experiments on the segmentation of brain magnetic resonance images show that the proposed model can gain better results in image segmentation.