DOIONLINE

DOIONLINE NO - IJEEDC-IRAJ-DOIONLINE-13017

Publish In
International Journal of Electrical, Electronics and Data Communication (IJEEDC)-IJEEDC
Journal Home
Volume Issue
Issue
Volume-6,Issue-7  ( Jul, 2018 )
Paper Title
A Platform Design for Acute Stroke Ischemia Brain Detection in Magnetic Resonance Imaging
Author Name
Fathia Aboudi, Salam Labidi, Aymen Mouelhi
Affilition
UTM, High Institute of Medical Technology in Tunisia (ISTMT), LRBTM Research Laboratory of Biophysics and Medical Technology, 9 Avenue Zouheir Essafi 1006 Tunis. Laboratory of Signal Image and Energy Mastery, ENSIT-University of Tunis, Tunisia., University of Tunis, 5 Av. Taha Hussein, 1008, Tunis, Tunisia.
Pages
62-68
Abstract
Public health is one of the major concerns at the international level. Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Every year, 12 million people are affected by stroke brain in worldwide and the value is increasing. Acute ischemia lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Brain Magnetic Resonance Imaging (MRI) brain is one of the essential non-invasive modalities for the diagnostic of this diseases. Indeed, diffusion weighted (DW) and perfusion weighted (PW) imaging are very helpful to detect acute stroke in early stages. This is a survery paper which presents a platform design of an automated segmentation using benchmark methods (Fuzzy C-Means (FCM), Otsu, Regions growing and Spatial FCM) in order to obtain a robust, rapid, efficient and precocious system detection of acute stroke lesions from MR images obtained from diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI). The validation purposes was performed by comparing resulting segmentation to the manual contours traced by an expert. Results show that the SFCM appeared in the plateform is 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 about one second. Keywords - Brain MRI, Acute Stroke Ischemia Brain, Automatic Segmentation, FCM, SFCM, Diffusion Weight Imaging, Perfusion Weight Imaging, Neuroimaging.
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