One of the most important subject in the processing MR image is segmentation, especially extraction of the brain regions, which is part of the decision of urgent operation on brain.This type medical operations need speed up process with maximum accuracy. In this study, brain is segmented by using k-means algorithm. A combination of global, adaptive thresholding techniques and at the next stage morphological operations were used for preprocessing. Moreover after this stage the main aim was setting out in the regional different of specified brain disorders to detect autism disease. Neuroimages which belong to 5 female patients in 17 years old who are diagnosed with autism and 10 female adolescents averaging 17 years old who have Typical Development were used. The parameters were slices consisted of 1.5 mm tickness dual-echo fast spin echo data sets that are acquired through MRI scanners. The quality and robutness of the results of this study depend on the homogenity of MRIs. Finally neuroimages were segmentated to gray matter and white matter and volumetric measuments were calculated for whole brain and of these issue types. To compare the results between the groups, Independent sample t-tests analysis results were assessed.