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A Radiomic feature-based Nipple Detection Algorithm on Digital Mammography.

Jiayu Jiang1,2, Yaqin Zhang3, Yao Lu1,2

  • 1School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China.

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|June 27, 2019
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Summary
This summary is machine-generated.

A new algorithm effectively detects nipples on digital mammograms (DM) using geometric and radiomic features. This method improves nipple localization accuracy, particularly for subtle cases, aiding breast lesion diagnosis.

Keywords:
digital mammogram (DM)multiple view analysisnipple detection

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • The nipple is a key anatomical landmark for aligning multiview mammograms in breast lesion diagnosis.
  • Accurate nipple localization is crucial for consistent image registration and analysis.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for detecting nipples on digital mammograms (DM).
  • To utilize pixel classification based on geometric and radiomic features for nipple detection.

Main Methods:

  • A two-tiered approach was developed: top hat transformation for obvious nipples and a revised rubber band straightening transformation with random forest classification for subtle nipples.
  • Features were extracted from breast boundary regions, and pixel classification was applied for nipple localization.

Main Results:

  • The algorithm achieved an average Euclidean distance of 2.69 mm for obvious nipples and 7.81 mm for subtle nipples compared to radiologist-identified locations.
  • Detection rates within a 10-mm radius were 97.61% for obvious nipples and 88.17% for subtle nipples.

Conclusions:

  • The proposed nipple detection algorithm is effective for digital mammography.
  • The method demonstrates particular efficacy in detecting subtle nipples, enhancing diagnostic capabilities.