基于多元信息的高斯混合模型左心室MR图像分割

水平集模型在核磁共振图像(MRI)分割中具有十分重要的地位。但由于MR图像往往具有弱边界和强噪音,传统的水平集模型用于图像分割时一般依据图像梯度信息,因而很难得到真实解。高斯混合模型使用了图像全局信息,能较好地处理弱边界问题。但传统的高斯混合模型仅使用了灰度值分布信息,未对像素的位置进行考虑,这使得其在处理噪音图像时效果并不是很理想。该文利用图像多种信息构造新的信息场,使得由信息场构造的高斯混合模型更能处理噪音等影响,同时防止从弱边界泄漏。在取得心脏内壁后构造能量方程,运用形状约束和图像信息以得到心脏外壁。对左心室MR图像分割实验表明该模型具有较好的分割效果。

MR Image Segmentation of Left Ventricle Based on the Multi-information Gaussian Mixture Model

The Level set method has consequence in the fields of image segmentations.As the traditional active contour methods only use the information of the edge,when it segments images with strong noise or with weak edges it is difficult to get the true edge.Gaussian mixture model uses the global information of the image,so it can do solve the problems of the weak edges.But the traditional Gaussian mixture model only uses the information of the histogram and not uses the information of the location of the pixel.So it is sensitive to the noise.This paper gives a method to make a new information field,which combines the information of the region,texture and region simulation.With the new information field the Gaussian mixture model can reduce the effect of the noise.In this paper the Gaussian mixture model is introduced to the Level set model and reduces the effect of the noise and prevents the curve over the weak edges.After getting the inner edge of the left ventricle,this paper uses the region and shape information to segment the out edge.Experiments on the segmentation of left ventricle magnetic resonance images show this model has better effect in image segmentation.