Open AccessSmall-window parametric imaging based on information entropy for ultrasound tissue characterization
Author(s) -
Po-Hsiang Tsui,
Chin Kuo Chen,
Wang-Chuang Kuo,
KingJen Chang,
Jui Fang,
Hsiang Yang,
Dean Chou
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep41004
Subject(s) - parametric statistics , entropy (arrow of time) , nakagami distribution , receiver operating characteristic , computer science , sliding window protocol , imaging phantom , medical imaging , breast imaging , ultrasound , artificial intelligence , pattern recognition (psychology) , window (computing) , mathematics , optics , physics , algorithm , statistics , acoustics , mammography , medicine , breast cancer , decoding methods , operating system , quantum mechanics , machine learning , fading , cancer
Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted ( n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.