Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification

Nidhal Bouaynaya; Jerzy Zielinski; Dan Schonfeld

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arXiv · 0906.3722

Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification

Nidhal Bouaynaya·Jerzy Zielinski·Dan Schonfeld

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Abstract

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.

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Nidhal Bouaynaya, Jerzy Zielinski, Dan Schonfeld. 2009-06-19. Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification. https://arxiv.org/abs/0906.3722

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