A Preliminary Study of the Mean Scatterer Spacing Estimation from Pellets using Wavelet-Based Cepstral Analysis

被引：0

作者：

Nasr, Remie ^{[1
]}

Falou, Omar ^{[1
,2
]}

Shahin, Ahmad ^{[1
]}

Wirtzfeld, Lauren ^{[3
]}

Berndl, Elizabeth ^{[3
]}

Kolios, Michael C. ^{[3
]}

机构：

[1] Amer Univ Culture & Educ, Lebanese Univ, Azm Ctr Res Biotechnol & Its Applicat DSST, Tripoli, Lebanon

[2] Amer Univ Culture & Educ, Lebanese Univ, Dept Sci, Tripoli, Lebanon

[3] Inst Biomed Engn Sci & Technol, Toronto, ON, Canada

来源：

2017 FOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN BIOMEDICAL ENGINEERING (ICABME) | 2017年

关键词：

cepstral analysis; wavelet transform; scatterer spacing; pellet; quantitative ultrasound; scatterer properties; tissue characterization; ultrasound backscatter;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Ultrasonic backscattered signals contain information regarding the scatterer structures of the imaged biological tissues; a uniform scatterer distribution could be represented by periodicities in the backscattered signals. This work aims to characterize these scatterer periodicities using wavelet improved cepstral analysis. This technique was tested on simulated ultrasound signals, where the periodicity was clearly visible. Simulation results indicate that this technique can effectively determine the value of the scatterer spacing. The technique was then tested on a HT-29 cell pellet, where the estimated scatterer spacing was found to be 17.67 +/- 3.85 mu m. Future work includes improving the technique with the aim of accurately estimating the mean scatterer spacing in tissues.

引用

页码：85 / 88

页数：4

共 45 条

[31] Biometrics from heart sounds: Evaluation of a new approach based on wavelet packet cepstral features using HSCT-11 database [J].

Abo-Zahhad, M. ;

Ahmed, Sabah M. ;

Abbas, Sherif N. .

COMPUTERS & ELECTRICAL ENGINEERING, 2016, 53 :346-358

[32] Polarisation analysis of magnetotelluric time series using a wavelet-based scheme: A method for detection and characterisation of cultural noise sources [J].

Escalas, M. ;

Queralt, P. ;

Ledo, J. ;

Marcuello, A. .

PHYSICS OF THE EARTH AND PLANETARY INTERIORS, 2013, 218 :31-50

[33] Wavelet-Based Determination of Cu and Pb in Water Samples using Potentiometric Stripping Analysis with a Lab-Made Potentiostat [J].

Martins, Valdomiro L. ;

Harrop Galvao, Roberto K. ;

de Araujo, Mario Cesar U. ;

Gaiao, Edvaldo da Nobrega .

JOURNAL OF THE BRAZILIAN CHEMICAL SOCIETY, 2009, 20 (08) :1561-1564

[34] A Comparative Analysis of Wavelet-Based FEMG Signal Denoising with Threshold Functions and Facial Expression Classification Using SVM and LSSVM [J].

Kehri, V ;

Awale, R. N. .

INTERNATIONAL JOURNAL OF ENGINEERING, 2020, 33 (07) :1249-1256

[35] An experimental comparison in the uncertainty estimation affecting wavelet-based signal analysis by means of the IEC-ISO guide and the random-fuzzy approaches [J].

Salicone, S ;

Tinarelli, R .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2006, 55 (03) :691-699

[36] Investigation into the Eroding Dry-band Arcing of Filled Silicone Rubber under DC using Wavelet-based Multiresolution Analysis [J].

Ghunem, Refat Atef ;

Jayaram, Shesha H. ;

Cherney, Edward A. .

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2014, 21 (02) :713-720

[37] Wavelet-Based Analysis of Subsidence Patterns and High-Risk Zone Delineation in Underground Metal Mining Areas Using SBAS-InSAR [J].

Li, Jiang ;

Tan, Zhuoying ;

Zeng, Nuobei ;

Xu, Linsen ;

Yang, Yinglin ;

Siddique, Aboubakar ;

Dang, Junfeng ;

Zhang, Jianbing ;

Wang, Xin .

LAND, 2025, 14 (05)

[38] Screening analysis of river seston downstream of an effluent discharge point using near-infrared reflectance spectrometry and wavelet-based spectral region selection [J].

De Medeiros, VM ;

Araújo, MCU ;

Galvao, RKH ;

Da Silva, EC ;

Saldanha, TCB ;

Toscano, IAS ;

De Oliveira, MDSR ;

Freitas, SKB ;

Neto, MM .

WATER RESEARCH, 2005, 39 (13) :3089-3097

[39] Multi-dimensional distribution network voltage collaborative estimation method using wavelet transform based multiscale analysis [J].

Ma, Haotian ;

Sheng, Wanxing ;

Liu, Keyan .

ELECTRIC POWER SYSTEMS RESEARCH, 2024, 231

[40] River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India [J].

Chakraborty, Swarnadeepa ;

Biswas, Sujata .

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2023, 37 (08) :3163-3184

← 1 2 3 4 5 →