Ischemic stroke detection in non-enhanced computed tomography examinations. (September 2016) - Alves, Allan F.F.; Sampaio, Ana J.M.; Maues, Nadine H.P.B.; de Oliveira, Marcela; Pavan], Ana L.M.; de Freitas, Carlos C.M.; Abdala, Nitamar; Pina, D.R.
Abstract : Introduction: Stroke is a worldwide concern, only in Brazil it accounts for 10% of all registered deaths. Early diagnosis is essential to avoid irreversible cerebral damage. Non-enhanced computed tomography (NECT) is the main diagnostic techniques used to diagnose stroke through the Alberta Stroke Program Early CT Score (ASPECTS) which is a subjective method that increases the detection rate. Purpose: The aim of this work was to implement an image segmentation and enhancement algorithm to detect ischemic stroke lesions in NECT scans. Materials and methods: We evaluated 10 patients diagnosed with ischemic stroke. Analyzes were performed in two axial slices, one at the level of the thalamus and basal ganglion and one adjacent to the top edge of the ganglionic structures. We used different image processing techniques such as morphological filters, discrete wavelet transform and Fuzzy C-means clustering to both enhance and distinguish ischemic tissues from normal brain tissues. Subjective analyzes were performed by a neuroradiologist according to the ASPECTS scale to quantify ischemic areas. These subjective analysis results were compared with objective analyzes performed by the computational algorithm. Results: Morphological filters actually improved the ischemic areas. The comparison in area of the ischemic region contoured by the neuroradiologist and by the computational algorithm showed no deviations greater than 12%. Conclusion: These results show the importance of a computer aided diagnosis software to assist neuroradiology decisions, especially in critical situations such as the choice of treatment for ischemic stroke. Disclosure: The Authors declare that there is no conflict of interests.