Classification of Breast Lesions In Ultrasound Imaging

We will develop a computer-assisted system to improve the ability of breast sonography to distinguifrom benign lesions. These are lesions originally detected by mammography and sent to ultrasound forThe lesions are biopsied when they are classified by ultrasound as solid or indeterminate, even thoubenign at biopsy. Because of the large numbers of breast biopsies being performed, even a small improf ultrasound to distinguish benign from malignant could result in significant decreases in numbershealth care costs, and patient morbidity. Our approach is to apply advanced image analysis techniquelesion images. In this Phase I project, we will analyze each lesion for three zones: background textand lesion itself rather than convention methods using one zone. The artificial neural network willfeatures for the classification of the malignancy of the lesion. A newly developed artificial visualconstructed specifically for ultrasound images. This neural network, which simulates human eye, willexperienced sonographers and will be tested for the analysis of ultrasound breast lesions. In Phasepossible features and analytical techniques, test our methods on a large database, and develop automworkstation to make the system usable in a clinical environment.