Li Shuan-qiang, Feng Qian-jin, Chen Wu-fan, Lin Ya-zhong
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2011 Jun;31(7):1164-8.
For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.
为了实现脑膜瘤磁共振（MR）图像的精确分割，我们提出了一种基于图割的新型交互式分割方法。提取了高维图像特征，并针对每个像素，通过利用加权K近邻分类器估计其源自肿瘤或背景区域的概率。基于这些概率，提出了一种新的能量函数。最后，使用图割最优框架求解能量函数。通过将该方法应用于脑膜瘤MR图像的分割进行评估，结果表明该方法与基于灰度信息的图割方法相比，显著提高了分割精度。
