A Wold decomposition-based autonomous system for detecting breast lesions in ultrasound images of the breast

Abstract

This paper presents an autonomous system for detecting lesions in the breast. The Wold decomposition algorithm described is used to decompose the RF echo of the breast into its diffuse and coherent components. The coherent component is modeled as a periodic sequence and the diffuse component is modeled as an autoregressive time series of low order. The parameters of the model are estimated from selected regions of the RF image and used as detection features. The database of images that was used contained 370 B-scan images from 52 patients, obtained in the Radiology department of the Thomas Jefferson Hospital. The pathologies of interest are carcinoma fibrocystic and stromal fibrosis disease and fibroadenoma. Empirical ROC techniques were used to evaluate the detection rate on single parameters of the model, such as the residual error variance and the autoregressive parameters of the diffuse component of the RF echo. The area under the empirical ROC curve for detecting lesion regions versus normal RF regions is 0.901. The area under the ROC curve for detecting carcinoma versus normal regions is 0.904. The corresponding areas for normal regions versus stromal fibrosis/fibrocystic regions and fibroadenoma regions are 0.942 and 0.899, respectively.