Abstract
The shape and arrangement of clustered microcalcifications help the radiologists to judge the likelihood of cancer being present. In general, malignant calcifications are very numerous, clustered, small, dot-like or elongated, and variable in size, shape, and density. In contrast, benign calcifications are generally larger, less numerous, more rounded, more diffusely distributed, and more homogeneous in size and shape. Starting from a segmentation algorithm that individually identifies the microcalcifications, we have used a bottom-up hierarchical algorithm to group them into clusters for a later characterization. This behavior leads to meaningfully differentiated shapes for both kinds of clusters. A Case-Based Reasoning classifier has been applied to classify the data and validate the cluster composition with promising results (at a false positive fraction of 10%, an 86% true positive fraction has been attained).
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Planiol, P., Martí, J., Español, J., Golobardes, E., Gay, J., Freixenet, J. (2003). Representing clustered microcalcifications by their cluster shape. In: Peitgen, HO. (eds) Digital Mammography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59327-7_57
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DOI: https://doi.org/10.1007/978-3-642-59327-7_57
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