  We propose a new model-based computer-aided diagnosis (CAD) system for tumor
detection and classification (cancerous v.s. benign) in breast images.
Specifically, we show that (x-ray, ultrasound and MRI) images can be accurately
modeled by two-dimensional autoregressive-moving average (ARMA) random fields.
We derive a two-stage Yule-Walker Least-Squares estimates of the model
parameters, which are subsequently used as the basis for statistical inference
and biophysical interpretation of the breast image. We use a k-means classifier
to segment the breast image into three regions: healthy tissue, benign tumor,
and cancerous tumor. Our simulation results on ultrasound breast images
illustrate the power of the proposed approach.
