School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
J Digit Imaging. 2013 Aug;26(4):786-96. doi: 10.1007/s10278-012-9568-1.
Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.
医学图像中肿瘤的高效分割在早期诊断和放射计划中具有重要的实际意义。本文提出了一种基于群体和个体统计信息的新的半自动分割方法，用于分割磁共振（MR）图像中的脑肿瘤。首先，提取高维图像特征。提出邻域成分分析来学习两个最优距离度量，分别包含群体和患者特定信息。在学习的最优距离度量下，通过 k-最近邻分类器估计每个像素属于前景（肿瘤）和背景的概率。通过这些概率构建分割的代价函数，并使用图割进行优化。最后，进行一些形态学操作以改善分割结果。我们的数据集包含 137 张脑部磁共振图像，其中 68 张用于训练，69 张用于测试。所提出的方法克服了由于肿瘤不均匀灰度分布引起的分割困难，即使肿瘤边缘模糊也能得到令人满意的结果。实验结果表明，该方法对脑肿瘤分割具有鲁棒性。
