This paper focuses on characterizing the internal intensity structure of pulmonary nodules in thin-section CT images for classification between benign and malignant nodules. This approach makes use of shape index curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. From the distribution of shape index, curvedness, and CT density over the 3D pulmonary nodule image a set of histogram features, and 3D texture features is computed to classify benign and malignant nodules. Linear discriminant analysis is used for classification and a receiver operating characteristic (ROC) analysis is used to evaluate the classification accuracy. The potential usefulness of the curvature-based features in the computer-aided differential diagnosis is demonstrated by using ROC curves as the performance measure.


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    Titel :

    Computer aided differential diagnosis of pulmonary nodules using curvature based analysis


    Beteiligte:
    Kawata, Y. (Autor:in) / Niki, N. (Autor:in) / Ohmatsu, H. (Autor:in) / Kusumoto, M. (Autor:in) / Kakinuma, R. (Autor:in) / Mori, K. (Autor:in) / Nishiyama, H. (Autor:in) / Eguchi, K. (Autor:in) / Kaneko, M. (Autor:in) / Moriyama, N. (Autor:in)


    Erscheinungsdatum :

    01.01.1999


    Format / Umfang :

    233779 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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