title: Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.
abstract: Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved.
authors.affiliation: Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. mmkhalilzadeh@gmail.com
authors.forename: Mohammad Mahdi
authors.initials: MM
authors.lastname: Khalilzadeh
authors.forename: Emad
authors.initials: E
authors.lastname: Fatemizadeh
authors.forename: Hamid
authors.initials: H
authors.lastname: Behnam
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity