A multiresolution wavelet analysis of digital mammograms
C.H. Chen, G. G. Lee · 1996
This paper discusses the significance of image segmentation via the combination of both statistical and nonstatistical methods based on the hierarchical framework of multiresolution wavelet analysis (MWA) and Gaussian Markov random fields (GMRF). Microcalculations and subtle mass regions are segmented via a fuzzy c-means (FCM) algorithm using localized features. For further enhancement, expected maximization and constrained optimization is applied to a Gibbs distribution defined from the FCM clustered image labels under a Bayesian framework. The effectiveness of this novel algorithm has been clearly illustrated by real mammographic images.