基于直方图梯度计算的T2加权脑部MR图像自动分割

Automatic segmentation of T2 weighted brain MRI based on histogram gradient calculation

针对脑部核磁共振图像分割问题,提出了一种直方图梯度计算方法。首先,对MR图像的直方图进行平滑处理,从而去除三个体素中出现的最低灰度级;然后,在预处理后的直方图上计算梯度;最后,计算对象数和其所在位置的梯度,并对图像进行自动分割。基于直方图处理进行梯度计算,大大降低了计算复杂度。在T2加权脑部MR图像上的实验结果表明,该方法可以有效地从二维和三维图像中提取出主要脑部区域,并在临床环境中获得的人类脑部MR图像上成功实施,分割效果优于其他几种现有分割算法。

For the issues of magnetic resonance images segmentation,this paper proposed a method based on histogram gradient calculation. Firstly,it smoothed the histogram of MR image so as to remove the lowest grayscale of three individuals.Then,it calculated the gradient on the preprocessed histogram. Finally,it segmented the image after calculating successfully number of objects and their gradients in which they lay. The proposed method was purely based on histogram processing for gradient calculation,so the computational complexity was reduced greatly. Experimental results on T2 weighted MR brain images show that the primary brain areas are extracted out efficiently from 2D and 3D images by the proposed method which has been successfully implemented on human brain MR images obtained in clinical environment. It has better segmentation efficiency than the several existing segmentation algorithms.