Journal article
Use of the K-distribution for classification of breast masses
Ultrasound in medicine & biology, v 26(9), pp 1503-1510
2000
PMID: 11179624
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The K-distribution had been introduced as a valid model to represent the statistics of the envelope of the backscattered echo from phantom and tissue. This paper investigates the efficacy of the parameters of this statistical model; namely, the effective number and the effective cross-section, to characterize breast lesions as benign or malignant. Based on the normalized values of the effective number and the effective scattering cross-section, images containing benign and malignant masses were classified for a data set from 52 patients having breast masses/lesions. The receiver operating characteristic (ROC) curves were then obtained to test the classification based on these two parameters. The results indicate that the parameters of the K-distribution may be useful in classification of the breast lesions as benign and malignant.
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Details
- Title
- Use of the K-distribution for classification of breast masses
- Creators
- P.M Shankar - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAV.A Dumane - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAJ.M Reid - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAV Genis - School of Biomedical Engineering, Sciences and Health Systems, Drexel University, Philadelphia, PA, USAF Forsberg - Division of Ultrasound, Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USAC.W Piccoli - Division of Ultrasound, Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USAB.B Goldberg - Division of Ultrasound, Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
- Publication Details
- Ultrasound in medicine & biology, v 26(9), pp 1503-1510
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; School of Biomedical Engineering, Science, and Health Systems; Engineering Technology
- Web of Science ID
- WOS:000166894200014
- Scopus ID
- 2-s2.0-0034496466
- Other Identifier
- 991014878272404721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Acoustics
- Radiology, Nuclear Medicine & Medical Imaging