Automatic Early Stroke Recognition Algorithm in CT Images
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.
KeywordsSupport Vector Machine Automatic Recognition Perfect Reconstruction Class Imbalance Problem Propose Extraction Method
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