Automatic ischemic stroke segmentation using various techniques

Abstract

Seven different methods aiming at automatic segmentation of human brain ischemic area in the computerized tomography scans are compared. The novel technique, based on the biologically inspired artificial neural networks architecture, is applied for the brain ischemic stroke recognition. The segmentation techniques were evaluated by the experts radiologists. The best viability showed Histogram, Gray level co-occurrence matrix, Mean and standard deviation methods, and Supervised Artificial Neural Networks techniquesKauno technologijos universitetasVilniaus Gedimino technikos universiteta