Studies on the use of non-Rayleigh statistics for ultrasonic tissue characterization.
The research groups at Drexel University and Thomas Jefferson University had proposed the use of non-Rayleigh statistics for tissue characterization. Previous work based on the hypothesis that the envelope of the backscattered echosignal from abnormal regions of the tissue is more likely to be K-distributed than Rayleigh distributed, used the parameter of the K-distribution, M, to distinguish between regions containing benign or malignant masses and normal ones. In this work the B-scan breast images of 19 patients were studied using this approach. Previous studies have also been extended to exploit the existence of non-uniform phase characteristics of the echosignal from scatterers with some regular spacings, such as those in a periodic or quasi-periodic alignment. Computer simulations were carried out to show that the phase statistics deviate significantly from uniform in the range of (0, 2 pi) if the imaging region contained a number of periodically aligned (regular lattice) scatterers along with a collection of randomly distributed scatterers resulting in a quasi-periodic arrangement. This methodology was then applied to B-scan images of the breasts to distinguish between benign and malignant masses. If benign lesions show some sort of quasi-periodic or regular structures in the tissue, they will present non-uniform phase characteristics while more randomly structured malignant masses will have uniform phase characteristics. It is seen that the K-distribution may be used to identify the abnormal regions in the breast images and information on the phase may be used to further separate the abnormal regions into benign and malignant ones.
Shankar
P M
PM
Biomedical Engineering & Science Institute, Philadelphia, PA, USA.
Molthen
R
R
Narayanan
V M
VM
Reid
J M
JM
Genis
V
V
Forsberg
F
F
Piccoli
C W
CW
Lindenmayer
A E
AE
Goldberg
B B
BB
D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D008327:Mammography; D015233:Models, Statistical; D014465:Ultrasonics; D016217:Ultrasonography, Mammary