  Pixel intensity is a widely used feature for clustering and segmentation
algorithms, the resulting segmentation using only intensity values might suffer
from noises and lack of spatial context information. Wavelet transform is often
used for image denoising and classification. We proposed a novel method to
incorporate Wavelet features in segmentation and clustering algorithms. The
conventional K-means, Fuzzy c-means (FCM), and Active contour without edges
(ACWE) algorithms were modified to adapt Wavelet features, leading to robust
clustering/segmentation algorithms. A weighting parameter to control the weight
of low-frequency sub-band information was also introduced. The new algorithms
showed the capability to converge to different segmentation results based on
the frequency information derived from the Wavelet sub-bands.
