A novel and automatic pectoral muscle identification algorithm for mediolateral oblique (MLO) view mammograms using ImageJ
This addresses the need for better breast cancer risk analysis tools, though it appears incremental as it builds on existing software for a specific medical imaging task.
The paper tackled the problem of inefficient and inaccurate pectoral muscle identification in MLO view mammograms by proposing a novel automatic algorithm using ImageJ, which showed promising results in validation with real-world data.
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.