Subcortical Region Segmentation using Fuzzy Based Augmented Lagrangian Multiphase Level Sets Method in Autistic MR Brain Images

In this work, the subcortical regions of control and autistic MR brain are segmented from the skull stripped images using Fuzzy C-Means (FCM) based Augmented Lagrangian (AL) multiphase level set method. The FCM method is used as he intensity discriminator for the multiphase level set method. The AL function avoids the re-initialization procedure. The segmented subcortical regions are validated with the ground truth images using dice similarity index. The texture features such as energy and entropy are calculated from the extracted cortical and subcortical regions. The results show that the multiphase level set method is able to segment the subcortical regions such as corpus callosum, brain stem and cerebellum. The dice similarity index gives above 0.85 for controls and 0.8 for autistic subjects. The texture feature energy calculated from the cortical region is high in autistics compared to the control subjects and vice versa in the case of entropy. The energy calculated from the subcortical regions is high in controls and entropy is high in autism subjects. Comparatively, the energyand entropy calculated from the total brain and brain stem gives significant variation (p<0.0001) between the control and autistic images. As the delayed growth of subcortical region is associated with high values of entropy, this study is clinically significant in the mass screening of neurodevelopmental disorders such as autism.