Anomaly depth detection in trans-admittance mammography: A formula independent of anomaly size or admittivity contrast

Tingting Zhang, Eunjung Lee, Jin Keun Seo

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Trans-admittance mammography (TAM) is a bioimpedance technique for breast cancer detection. It is based on the comparison of tissue conductivity: cancerous tissue is identified by its higher conductivity in comparison with the surrounding normal tissue. In TAM, the breast is compressed between two electrical plates (in a similar architecture to x-ray mammography). The bottom plate has many sensing point electrodes that provide two-dimensional images (trans-admittance maps) that are induced by voltage differences between the two plates. Multi-frequency admittance data (Neumann data) are measured over the range 50 Hz-500 kHz. TAM aims to determine the location and size of any anomaly from the multi-frequency admittance data. Various anomaly detection algorithms can be used to process TAM data to determine the transverse positions of anomalies. However, existing methods cannot reliably determine the depth or size of an anomaly. Breast cancer detection using TAM would be improved if the depth or size of an anomaly could also be estimated, properties that are independent of the admittivity contrast. A formula is proposed here that can estimate the depth of an anomaly independent of its size and the admittivity contrast. This depth estimation can also be used to derive an estimation of the size of the anomaly. The proposed estimations are verified rigorously under a simplified model. Numerical simulation shows that the proposed method also works well in general settings.

Original languageEnglish
Article number045003
JournalInverse Problems
Issue number4
Publication statusPublished - 2014 Apr

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Signal Processing
  • Mathematical Physics
  • Computer Science Applications
  • Applied Mathematics


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