Segmentation of brain tissues from magnetic resonance (MR) images plays a crucial role in medical image processing. In this paper, we propose an automatic unsupervised segmentation method integrating wavelet transform with self-organizing map for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of wavelet transform and spatial position information. Then, an adaptive growing self-organizing tree map (AGSOTM) is presented, which adaptively captures the complicated spatial layout of the individual tissues, and overcomes the problem of overlapping grey-scale intensities for different tissues. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with other algorithms.
A blind sub-carrier recognition algorithm of TT&C communication is proposed based on Negentropy-maximization in terms of recognition of TT&C signals for military TT&C communication information scout. First, the basic principle of the ICA is discussed in this paper. Using maximum Negentropy approximation of differential Negentropy, an objective function for ICA is introduced and a Fast-ICA algorithm based on maximum Negentropy is presented. Based on analyzing Fast-ICA algorithm deeply, this paper expounds a new method to adopt it in the recognition of TT&C signals of satellite. Simulation results in MATLAB show its better performance and efficiency in the mixed TT&C signals of satellite recognition, proving its good convergence and robust.
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