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Pectoral muscle identification is often required for breast cancer riskanalysis, such as estimating breast density. Traditional methods areoverwhelmingly based on manual visual assessment or straight line fitting forthe pectoral muscle boundary, which are inefficient and inaccurate sincepectoral muscle in mammograms can have curved boundaries.  This paper proposes a novel and automatic pectoral muscle identificationalgorithm for MLO view mammograms. It is suitable for both scanned film andfull field digital mammograms. This algorithm is demonstrated using a publicdomain software ImageJ. A validation of this algorithm has been performed usingreal-world data and it shows promising result.

A novel and automatic pectoral muscle identification algorithm for mediolateral oblique (MLO) view mammograms using ImageJ