Computer aided diagnosis techniques in medical imaging are developed for
the automated differentiation between benign and malignant lesions and go
beyond computer aided detection by providing cancer likelihood for a
detected lesion given image and/or patient characteristics. A computer
aided detection and diagnosis algorithm for mammographic calcification
clusters is developed and evaluated. The emphasis is on the diagnostic
component although the algorithm includes automated detection,
segmentation, and classification steps based on wavelet filters and
artificial neural networks. Classification features are selected
primarily from descriptors of the morphology of the individual
calcifications and the distribution of the cluster as well as patient's
demographics as input to the network. Te selected features are robust
morphological and distributional descriptors, relatively insensitive to
segmentation and detection errors such as false positive signals and
variations among imaging sources or imaging equipment.