This paper presents a novel method of detecting and describing pathological changes that can be visualized on dynamic computer tomography brain maps (perfusion CT). The system was tested on a set of dynamic perfusion computer tomography maps. Each set consisted of two perfusion maps (CBF, CBV and TTP for testing the irregularity detection algorithm) and one CT brain scan (for the registration algorithm) from 8 different patients with suspected strokes. In 36 of the 84 brain maps, abnormal perfusion was diagnosed. The results of the algorithm were compared with the findings of a team of two radiologists. All of the CBF and CBV maps that did not show a diagnosed asymmetry were classified correctly (i.e. no asymmetry was detected). In four of the TTP maps the algorithm found asymmetries, which were not classified as irregularities in the medical diagnosis; 84.5% of the maps were diagnosed correctly (85.7% for the CBF, 85.7% for the CBV and 82.1% for the TTP); 75% of the errors in the CBF maps and 100% of the errors in the CBV and the TTP maps were caused by the excessive detection of asymmetry regions. Errors in the CBFs and the CBVs were eliminated in cases in which the symmetry axis was selected manually. Subsequently, 96.4% of the CBF maps and 100% of the CBV maps were diagnosed correctly.
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