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Breast cancer is the most common cancer in women. Many countries—including the UK—offer asymptomatic screening for the disease. The interpretation of mammograms is a visual task and is subject to human error. Computer-aided image interpretation has been proposed as a way of helping radiologists perform this difficult task. Shape and texture features are typically classified into true or false detections of specific signs of breast cancer. This thesis promotes an alternative approach where any deviation from normal appearance is marked as suspicious, automatically including all signs of breast cancer. This approach requires a model of normal mammographic appearance. Statistical models allow deviation from normality to be measured within a rigorous mathematical framework. Generative models make it possible to determine how and why a model is successful or unsuccessful. This thesis presents two generative statistical models. The first treats mammographic appearance as a stationary texture. The second models the appearance of entire mammograms. Psychophysical experiments were used to evaluate synthetic textures and mammograms generated using these models. A novelty detection experiment on real and simulated data shows how the model of local texture may be used to detect abnormal features.