Accessibility navigation


Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements

Yin, X.X., Ng, B.W.H., Ferguson, B., Abbott, D. and Hadjiloucas, S. (2007) Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements. Journal of Biological Systems, 15 (4). pp. 551-571. ISSN 0218-3390

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1142/S0218339007002374

Abstract/Summary

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:15490
Uncontrolled Keywords:Terahertz, Auto Regressive Moving Average (ARMA), Yule-Walker algorithm, Prony's method, soft threshold wavelet shrinkage de-noising, discrete wavelet transform (DWT), wavelet packet transform (WPT), Mahalanobis distance classifier , SPECTROSCOPY

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation