This paper describes a segment-based asymmetry feature detection approach for three-dimensional positron emission tomography (PET) brain images to automatically extract pathological lesions. The method consists of three stages: preprocessing, segmentation, and asymmetry detection. The method was tested on simulation and clinical data sets and a per-pixel asymmetry feature detection is experimentally compared with our per-segment approach and the per-segment method is shown to produce fewer false positives and better demarcation in the PET data examples presented.


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    Titel :

    Pathological lesion detection in 3D dynamic PET images using asymmetry


    Beteiligte:
    Zhe Chen, (Autor:in) / Dagan Feng, (Autor:in) / Weidong Cai, (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    322337 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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