Brain MR Image Segmentation with Spatial Constrained K-mean Algorithm and Dual-Tree Complex Wavelet Transform

• Published in: Journal of medical systems Vol. 38; no. 9; pp. 93 - 6
• Main Authors: Zhang, Jingdan, Jiang, Wuhan, Wang, Ruichun, Wang, Le
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.09.2014; Springer Nature B.V
• Subjects: Algorithms; Brain; Constraints; Deterioration; Education & Training; Health Informatics; Health Sciences; Humans; Image Enhancement - methods; Image processing systems; Image segmentation; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Neuroimaging - methods; NMR; Nuclear magnetic resonance; Segmentation; Statistics for Life Sciences; Wavelet Analysis; Wavelet transforms; MR images; Medical image segmentation; Dual-tree complex wavelet transform; K-mean algorithm
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-014-0093-2

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.