title: Brain MR image segmentation with spatial constrained K-mean algorithm and dual-tree complex wavelet transform.
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.
authors.affiliation: Department of Electronics and Communication, Shenzhen Institute of Information Technology, Shenzhen, 518172, China, zhangjd358@163.com.
authors.forename: Jingdan
authors.initials: J
authors.lastname: Zhang
authors.forename: Wuhan
authors.initials: W
authors.lastname: Jiang
authors.forename: Ruichun
authors.initials: R
authors.lastname: Wang
authors.forename: Le
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D058067:Wavelet Analysis