Analysis of sub-cortical regions in cognitive processing using fuzzy c-means clustering and geometrical measure in autistic MR images

Magnetic Resonance (MR) imaging is an indispensable approach for obtaining the structural information of sub-cortical regions of brain. In this work, fuzzy c-means clustering (FCM) is used to segment the sub-cortical regions of brain such as Corpus Callosum (CC) and Brain Stem (BS). The geometrical measure area is calculated from the extracted regions and correlated with the clinical Intelligent Quotient (IQ) values. The corpus callosum area gives distinct variation between control and autistic subjects (p=0.006). Also, the CC area of autistic subjects is correlated with the verbal IQ value (R=-0.37). The area of brain stem is less statistically significant (p=0.02) compared to the CC area in discriminating the subjects. Also, BS area of autistic subjects gives a correlation of R=-0.29 with the performance IQ. As the reduced CC and BS area are related with cognitive dysfunctions, this framework can be used for the automated diagnosis of autism like neural disorders.