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    Volume 46, Issue 4 (May 2018)

    Special Issue Paper

    Studying the Statistics of Natural X-ray Pictures

    (Received 14 June 2017; accepted 28 March 2018)

    Published Online: 14 May 2018

    CODEN: JTEVAB

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    Abstract

    In this article, we have studied and analyzed the statistics of both pristine and distorted bandpass X-ray images. In the past, we have shown that the statistics of natural, bandpass-filtered visible light (VL) pictures, commonly expressed by natural scene statistic (NSS) models, can be used to create remarkably powerful, perceptually relevant predictors of perceptual picture quality. We find that similar models can be developed that apply quite well to X-ray image data. We have also studied the potential of applying these statistical X-ray NSS models to the design of algorithms for automatic image quality prediction of X-ray images, such as might occur in security, medicine, and material inspection applications. As a demonstration of the discrimination power of these models, we devised an application of NSS models to an image modality classification task, whereby VL, X-ray, infrared, and millimeter-wave images can be effectively and automatically distinguished. Our study is conducted on a dataset of X-ray images made available by the National Institute of Standards and Technology.

    Author Information:

    Gupta, Praful
    Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX

    Glover, Jack L.
    National Institute of Standards and Technology, Gaithersburg, MD

    Paulter, Nicholas G.
    National Institute of Standards and Technology, Gaithersburg, MD

    Bovik, Alan C.
    Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX


    Stock #: JTE20170345

    ISSN:0090-3973

    DOI: 10.1520/JTE20170345

    Author
    Title Studying the Statistics of Natural X-ray Pictures
    Symposium ,
    Committee F12