Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification

TL;DR

This paper introduces a 2D ARMA modeling approach for breast cancer detection and classification in medical images, utilizing statistical inference and a k-means classifier to distinguish tissue types.

Contribution

It presents a novel 2D ARMA model-based CAD system with parameter estimation and classification methods for breast tumor detection.

Findings

Effective modeling of breast images with 2D ARMA fields

Accurate segmentation of tissue types using k-means

Successful application on ultrasound images

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.