Computer-aided evaluation of screening mammograms based on local texture models
IEEE Transactions on Image Processing
Preprocessing of screening mammograms based on local statistical models
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
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Assuming local and shift-invariant texture properties we describe the statistical dependencies between pixels by a joint probability density of gray-levels within a suitably chosen observation window. We estimate the unknown multivariate density in the form of a Gaussian mixture of product components from data obtained by shifting the observation window. Obviously, the size of the window should be large to capture the low-frequency properties of textures but, on the other hand, the increasing dimension of the estimated mixture may become prohibitive. By considering a subspace approach based on a structural mixture model we can increase the size of the observation window while keeping the computational complexity in reasonable bounds.