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.
@article{arxiv.0906.3722,
title = {Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification},
author = {Nidhal Bouaynaya and Jerzy Zielinski and Dan Schonfeld},
journal= {arXiv preprint arXiv:0906.3722},
year = {2009}
}