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