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Author: search.filters.author.Withayachumnankul, W.Subject: search.filters.subject.T-rays

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    T-ray relevant frequencies for osteosarcoma classification
    (2006-04-17)
    Withayachumnankul, W.
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    Ferguson, B.
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    Rainsford, T.
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    Findlay, D.
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    Mickan, S. P.
    We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.
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    T-rays in biomedicine and security
    (2005-12-01)
    Rainsford, T.
    ;
    Png, G. M.
    ;
    Withayachumnankul, W.
    ;
    Ferguson, B.
    ;
    Mickan, S. P.
    With a number of commercial systems becoming increasingly available, and improved generation and detection techniques T-ray systems are increasingly finding new applications. One of the main technical challenges is to make systems low cost, compact and easy to use. In this paper we suggest some novel solutions for parameter extraction, image enhancement, improved signal-to-noise ratio and classification schemes. These tools are useful in a number of biomedical and security applications. © 2005 IEEE.