Ischemic Stroke Modeling: Multiscale Extraction of Hypodense Signs
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Basic concepts of knowledge-based image understanding
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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The subject of the reported study was automatic recognition of early ischemic stroke lesions in CT scans. Proposed extraction method was based on the investigated specificity of tissue texture features in hypothetical penumbra regions. Prediction of such regions was estimated by initial hypodensity enhancement procedure. Block-oriented areas of selected brain tissue were analyzed in both source and multiscale-processed data domains. The extraction and selection of well differentiating features were fundamental effort to verify research hypothesis that acute ischemic tissue is noticeably altered in CT imaging. Moreover, various classifiers were examined on large feature data sets. Limitations and shortcomings caused by a class imbalance problem were considered. Experimental verification of designed and implemented recognition procedures is the main input of this paper.