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.
Pathological lesion detection in 3D dynamic PET images using asymmetry
01.01.2003
322337 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Pathological Lesion Detection in 3D Dynamic PET Images Using Asymmetry
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