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Classification of benign/malignant microcalcification clusters is a major diagnostic challenge for radiologists. Clinical studies have revealed that the shape of the cluster, and the spatial distribution of individual microcalcifications within it, are important indicators of its malignancy. However, mammographic images of clustered microcalcifications confound their three-dimensional (3-D) distribution with image projection and breast compression. This paper presents a novel model-based method for reconstructing microcalcification clusters in 3-D from two mammographic views (cranio-caudal and medio-lateral oblique--"shoulder to the opposite hip" or lateral-medio). We develop a 3-D breast representation and a parameterised breast compression model which constraints geometrically the possible 3-D positions of a calcification in a two-dimensional image. Corresponding calcifications in the two views are matched using an estimate of the calcification volume. Both the geometric constraint and the matching criterion are utilized in the final reconstruction step to build the 3-D reconstructed clusters. Validation experiments are described using 30 clusters to verify the individual steps of the model, and results consistent with known ground truth are obtained. Some of the approximations in the model and future work are discussed in the concluding section.


Journal article


IEEE Trans Med Imaging

Publication Date





479 - 489


Algorithms, Breast Diseases, Calcinosis, Female, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Mammography