Ultrawideband microwave breast cancer detection: a detection-theoretic approach using the generalized likelihood ratio test

Microwave imaging has been suggested as a promising modality for early-stage breast cancer detection. In this paper, we propose a statistical microwave imaging technique wherein a set of generalized likelihood ratio tests (GLRT) is applied to microwave backscatter data to determine the presence and...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2005-07, Vol.52 (7), p.1237-1250
Main Authors: Davis, S.K., Tandradinata, H., Hagness, S.C., Van Veen, B.D.
Format: Article
Language:English
Subjects:
Algorithms
Array signal processing
Backscatter
biomedical electromagnetic imaging
Breast cancer
breast cancer detection
Breast Neoplasms - diagnosis
Breast Neoplasms - physiopathology
Cancer detection
clutter
Computer Simulation
Covariance matrix
FDTD methods
generalized likelihood ratio test (GLRT)
Humans
Image Interpretation, Computer-Assisted - methods
Imaging phantoms
Imaging, Three-Dimensional - methods
Likelihood Functions
Microwave imaging
microwave measurements
Microwave theory and techniques
Microwaves
Models, Biological
Models, Statistical
Phantoms, Imaging
Reproducibility of Results
Scattering
Sensitivity and Specificity
Studies
Testing
tissue phantoms
Tumors
Ultra wideband technology
ultrawide-band (UWB) radar
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Microwave imaging has been suggested as a promising modality for early-stage breast cancer detection. In this paper, we propose a statistical microwave imaging technique wherein a set of generalized likelihood ratio tests (GLRT) is applied to microwave backscatter data to determine the presence and location of strong scatterers such as malignant tumors in the breast. The GLRT is formulated assuming that the backscatter data is Gaussian distributed with known covariance matrix. We describe the method for estimating this covariance matrix offline and formulating a GLRT for several heterogeneous two-dimensional (2-D) numerical breast phantoms, several three-dimensional (3-D) experimental breast phantoms, and a 3-D numerical breast phantom with a realistic half-ellipsoid shape. Using the GLRT with the estimated covariance matrix and a threshold chosen to constrain the false discovery rate (FDR) of the image, we show the capability to detect and localize small (
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2005.847528