Zhang Jingdan, Jiang Wuhan, Wang Ruichun, Wang Le
Department of Electronics and Communication, Shenzhen Institute of Information Technology, Shenzhen, 518172, China,
J Med Syst. 2014 Sep;38(9):93. doi: 10.1007/s10916-014-0093-2. Epub 2014 Jul 4.
In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms.
在脑部磁共振成像（MR）图像中，噪声和低对比度会显著降低分割结果的质量。在本文中，我们提出了一种将双树复小波变换（DT-CWT）与K均值算法相结合的自动无监督分割方法，用于脑部MR图像分割。首先，基于图像强度、DT-CWT的低频子带和空间位置信息构建多维特征向量。然后，提出一种空间约束K均值算法作为分割系统。通过使用模拟和真实T1加权MR图像进行大量实验，对所提方法进行了验证，并与当前最先进的算法进行了比较。
