In this work, subcortical regions of autistic magnetic resonance brain images are analyzed using multiphase level set method. The images considered in this work are obtained from autism brain image data exchange database. The subcortical regions such as corpus callosum, cerebellum, and brain stem are segmented from the cortical region using Fuzzy c-means (FCM)-based multiphase level set method. FCM with three cluster center is used as the intensity discriminator and the evolution of the level set curve is regularized by a distance function. The results show that the multiphase level set method is able to segment the desired subcortical regions. The results are validated with the ground truth images. The average similarity values are found to be 0.85. The segmented subcortical regions of autistic have reduced tissue area and are distinct from the controls (p < 0.0001). Further, it is observed that the subcortical area gives comparable results with clinical intelligent quotient values and is able to discriminate the controls and autistic subjects. As the feature area extracted from brain subcortical regions are significant, this study seems to be clinically helpful in mass screening of autistic subjects.