Automatic brain matter segmentation of computed tomography images using a statistical model: A tool to gain working time!

Brain computed tomography (CT) is useful diagnostic tool for the evaluation of several neurological disorders due to its accuracy, reliability, safety and wide availability. In this field, a potentially interesting research topic is the automatic segmentation and recognition of medical regions of interest (ROIs). Herein, we propose a novel automated method, based on the use of the active appearance model (AAM) for the segmentation of brain matter in CT images to assist radiologists in the evaluation of the images.

Elman neural network for the early identification of cognitive impairment in Alzheimer's disease.

Early detection of dementia can be useful to delay progression of the disease and to raise awareness of the condition. Alterations in temporal and spatial EEG markers have been found in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Herein, we propose an automatic recognition method of cognitive impairment evaluation based on EEG analysis using an artificial neural network (ANN) combined with a genetic algorithm (GA).