Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
A data distributed parallel algorithm for nonrigid image registration
Parallel Computing
Cognitive techniques in medical information systems
Computers in Biology and Medicine
Modern Computational Intelligence Methods for the Interpretation of Medical Images
Modern Computational Intelligence Methods for the Interpretation of Medical Images
Augmented reality approaches in intelligent health technologies and brain lesion detection
ARES'11 Proceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems
Segmentation and visualization of tubular structures in computed tomography angiography
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
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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.