Detecting Stellate Lesions in Mammograms via Statistical Models

Malignant breast lesions in x–ray mammograms are often characterised by abnormal patterns of linear structures. Architectural distortions and stellate lesions are examples of patterns frequently presenting with an appearance of radiating lines. Attempts to automatically detect these abnormalities have generally concentrated on features of known importance, such as radiating linear structure concurrency, spread of focus and radial distance. We describe a generic representation of patterns of oriented lines that is both complete and uncommitted. Our representation places no emphasis on the features known to be important, yet clearly incorporates them. Statistical models, based on factor analysis, are trained on representations extracted from stellate lesions to detect abnormal patterns of linear structures. We present an application of the technique to a set of 129 mammograms containing 29 stellate lesions. In addition, we describe how directional recursive median filtering can be applied at a number of scales and how the resulting scale orientation signatures can be used to train a model to detect the central masses of stellate lesions. Results are provided for a set of 53 mammograms containing 26 stellate lesions. The combined results of the two techniques are presented and simple methods of combination are discussed. Selecting a single operating point for mass detection in combination with the oriented pattern technique improves the overall accuracy to a sensitivity of 100% at zero false positives per image for structures of diameter 16mm and above.