This article presents a novel algorithm for image seg- been developed for classification purposes. In addition, many mentation via the use of the multiresolution wavelet analysis and the authors have discovered significant advantages in the use of the expectation maximization (EM) algorithm. The development of a multiresolution concept ( 4,5 ) . Brazkovic and Neskovic presented multiresolution wavelet feature extraction scheme is based on the the Gaussian pyramid and fuzzy linking method for the adaptive Gaussian Markov random field (GMRF) assumption in mammo- detection of cancerous changes in mammograms (6). graphic image modeling. Mammographic images are hierarchically Recently, as a result of cross-fertilization of innovative ideas decomposed into different resolutions. In general, larger breast le- from image processing, spatial statistics, and statistical physics, sions are characterized by coarser resolutions, whereas higher resolu- a significant amount of research activity on image modeling and tions show finer and more detailed anatomical structures. These hier- archical variations in the anatomical features displayed by multiresolu- segmentation has also been concentrated on the two-dimensional tion decomposition are further quantified through the application of ( 2D ) Markov random field ( MRF ) . Although many of the poten- the Gaussian Markov random field. Because of its uniqueness in local- tials of MRF had been envisioned by the early works of Levy ity, adaptive features based on the nonstationary assumption of (7), McCormick and Jayaramamrhy (8), and Abend et al. (9), GMRF are defined for each pixel of the mammogram. Fibroadenomas exploitation of the powers of the MRF was not possible until are then segmented via the fuzzy C-means algorithm using these significant recent advances occurred in the appropriate mathemat- localized features. Subsequently, the segmentation results are further ical and computational tools. Chellappa and Kashyap (10 ) suc- enhanced via the introduction of a maximum a posteriori (MAP) seg- cessfully applied the noncausal autoregressive ( NCAR ) model mentation estimation scheme based on the Bayesian learning para-