Abstract In this paper, we propose a method of detecting liver cancers from dynamic X-ray computed tomography (CT) images based on a two-dimensional histogram analysis. In the diagnosis of a liver, a doctor examines dynamic CT images. These consist of four images, namely the pre-contrast phase, early phase, portal phase, and late phase ones, which are taken sequentially within a few minutes. Since the early and late phase images are important for diagnosing liver cancer, our method refers to both of them for detecting suspicious regions and eliminating false positives. First, it extracts liver cancer candidates by applying an adaptive neighbor type filter to the late phase image. Then, precise cancerous regions are specified by a region forming method. Most of the false positive regions are eliminated by two-dimensional histogram analysis of each region of interest. We applied the proposed method to 21 dynamic CT images. The results showed that sensitivity was 100% and there were 0.33 false positives per case on average.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Spatiotemporal Density Feature Analysis to Detect Liver Cancer from Abdominal CT Angiography


    Beteiligte:
    Mekada, Yoshito (Autor:in) / Wakida, Yuki (Autor:in) / Hayashi, Yuichiro (Autor:in) / Ide, Ichiro (Autor:in) / Murase, Hiroshi (Autor:in)


    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Spatiotemporal Density Feature Analysis to Detect Liver Cancer from Abdominal CT Angiography

    Mekada, Y. / Wakida, Y. / Hayashi, Y. et al. | British Library Conference Proceedings | 2006


    Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

    Zhang, W. / Fang, X. / Yang, X. et al. | British Library Online Contents | 2007


    Learning to detect dynamic feature points

    Park, Min-Gyu / Yoon, Ju Hong / Park, Jonghee et al. | IEEE | 2017


    A Probabilistic Tensor Factorization Approach to Detect Anomalies in Spatiotemporal Traffic Activities

    Wang, Xudong / Fagette, Antoine / Sartelet, Pascal et al. | IEEE | 2019


    Feature article: An approach to detect GNSS spoofing

    Broumandan, Ali / Siddakatte, Ranjeeth / Lachapelle, Gerard | IEEE | 2017