期刊
JOURNAL OF BIOLOGICAL SYSTEMS
卷 15, 期 4, 页码 551-571出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218339007002374
关键词
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
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%.
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