Modifies standard clustering and segmentation algorithms to use wavelet sub-band features with a weighting parameter for low-frequency information, enabling frequency-dependent segmentation results.
4; the proposed ACWE-W algorithm was tested on the simulated Quantitative Bone SPECT image using the NURBS-based XCAT phantom [16], an example slice is shown in Fig
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Feature-Based Image Clustering and Segmentation Using Wavelets
Modifies standard clustering and segmentation algorithms to use wavelet sub-band features with a weighting parameter for low-frequency information, enabling frequency-dependent segmentation results.