| Title | Classification of breast tissue by electrical impedance spectroscopy |
| Publication Type | Journal Article |
| 2000 |
| Authors | Estrela Da Silva, J, Marques De Sá, JP, Jossinet, J |
| Journal | Medical and Biological Engineering and ComputingMed. Biol. Eng. Comput. |
| Volume | 38 |
| Issue | 1Stevenage, United Kingdom |
| Pagination | 26 - 30 |
| Date Published | 2000/// |
| 01400118 (ISSN) |
| article, Breast, breast cancer, Breast Neoplasms, cancer classification, Data classification, Discriminant Analysis, Electric Impedance, Electrical impedance spectroscopy, Electrodiagnosis, Feature extraction, Female, human, Humans, impedance, Impedance spectroscopy, major clinical study, Medical imaging, Signal Processing, Computer-Assisted, Spectroscopy, Statistical analysis, Statistical methods, Tissue, Tissue characterisation |
| Electrical impedance spectroscopy is a minimally invasive technique that has clear advantages for living tissue characterisation owing to its low cost and ease of use. The present paper describes how this technique can be applied to breast tissue classification and breast cancer detection. Statistical analysis is used to derive a set of rules based on features extracted from the graphical representation of electrical impedance spectra. These rules are used hierarchically to discriminate several classes of breast tissue. Results of statistical classification obtained from a data set of 106 cases representing six classes of excised breast tissue show an overall classification efficiency of ~92% with carcinoma discrimination > 86%.Electrical impedance spectroscopy is a minimally invasive technique that has clear advantages for living tissue characterization owing to its low cost and ease of use. The present paper describes how this technique can be applied to breast tissue classification and breast cancer detection. Statistical analysis is used to derive a set of rules based on features extracted from the graphical representation of electrical impedance spectra. These rules are used hierarchically to discriminate several classes of breast tissue. Results of statistical classification obtained from a data set of 106 cases representing six classes of excised breast tissue show an overall classification efficiency of approximately 92% with carcinoma discrimination >86%.
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