Abstract
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.
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This project is supported in part by Shenzhen Science and Technology plan Project (JCYJ20120615101059717), and Project of Shenzhen Institute of Information Technology (YB201009, SYS201004)
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Zhang, J., Jiang, W., Wang, R. et al. Brain MR Image Segmentation with Spatial Constrained K-mean Algorithm and Dual-Tree Complex Wavelet Transform. J Med Syst 38, 93 (2014). https://doi.org/10.1007/s10916-014-0093-2
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DOI: https://doi.org/10.1007/s10916-014-0093-2