Abstract
Public health is one of the most concerns at the worldwide. Brain ischemic stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. It occurs in most stroke patients. Brain Magnetic Resonance Imaging (MRI) is one of the essential non-invasive modalities that provide a contrast imaging to visualize and detections lesions. Brain ischemic stroke segmentation in MRI has attracted the attention of medical doctors and researches since variations in structural and contrast of medical data. Several proposals have been designed throughout the years comprising a different strategy of brain segmentation. In particular, in this paper we analyse a segmentation methods used for detection and localization brain ischemic stroke. That the goal has been presented to differentiate between the lesions with the normal region. The Spatial Fuzzy C Means (SFCM) and methods based regions are developed in order to obtain a robust, rapid, efficient, precious and precocious detection of acute stroke lesion from images data issues by MRI with diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI). The validation purpose was performed by comparing resulting segmentation to the manual contours traced by an expert. Results show that the SFCM appeared efficient in detection of acute with a accuracy value of 99.1% in PWI-MTT and of 47.44% in DWI and an timing average in order to one second. However, the accuracy rate of regions growing in order to 17.40% in DWI and to 71.30% in PWI.
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Aboudi, F., Drissi, C., Kraiem, T. (2019). Brain Ischemic Stroke Segmentation from Brain MRI Between Clustering Methods and Region Based Methods. In: Farhaoui, Y., Moussaid, L. (eds) Big Data and Smart Digital Environment. ICBDSDE 2018. Studies in Big Data, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-030-12048-1_16
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DOI: https://doi.org/10.1007/978-3-030-12048-1_16
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