Distinguishing Lesions from Posterior Acoustic Shadowing in Breast Ultrasound via Non-Linear Dimensionality Reduction

Breast ultrasound (US) in conjunction with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. While breast US has certain advantages over digital mammography, it suffers from image artifacts such as posterior acoustic shadowing (PAS), presence of which often obfuscates lesion margins. Since classification of lesions as either malignant or benign is largely dictated by lesion's shape and margin characteristics, it is important to distinguish lesion area from PAS. This paper represents the first attempt to extract and identify those image features that can help distinguish between lesion and PAS. Our methodology comprises of extracting over 100 statistical, gradient, and Gabor features at multiple scales and orientations at every pixel in the breast US image. Adaboost, a powerful feature ensemble technique is used to discriminate between lesions and PAS by combining the different image features. A non-linear dimensionality reduction method called Graph Embedding is then used to visualize separation and inter-class dependencies between lesions and PAS in a lower dimensional space. Results of quantitative evaluation on a database of 45 breast US images indicate that our methodology allows for greater discriminability between the lesion and PAS classes compared to that achievable by any individual image texture or intensity feature.