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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    Title :

    Pathological lesion detection in 3D dynamic PET images using asymmetry


    Contributors:
    Zhe Chen, (author) / Dagan Feng, (author) / Weidong Cai, (author)


    Publication date :

    2003-01-01


    Size :

    322337 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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