Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm.

A spatially variant finite mixture model is proposed for pixel labeling and image segmentation. For the case of spatially varying mixtures of Gaussian density functions with unknown means and variances, an expectation-maximization (EM) algorithm is derived for maximum likelihood estimation of the pixel labels and the parameters of the mixture densities, An a priori density function is formulated for the spatially variant mixture weights. A generalized EM algorithm for maximum a posteriori estimation of the pixel labels based upon these prior densities is derived.

A regional registration technique for automated interval change analysis of breast lesions on mammograms.

Analysis of interval change is a useful technique for detection of abnormalities in mammographic interpretation. Interval change analysis is routinely used by radiologists and its importance is well-established in clinical practice. As a first step to develop a computerized method for interval change analysis on mammograms, we are developing an automated regional registration technique to identify corresponding lesions on temporal pairs of mammograms.

Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

Purpose: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' classification of malignant and benign masses seen on mammograms.

Materials And Methods: The authors previously developed an automated computer program for estimation of the relative malignancy rating of masses. In the present study, the authors conducted observer performance experiments with receiver operating characteristic (ROC) methodology to evaluate the effects of computer estimates on radiologists' confidence ratings.