Abstract
Identification of human brain structures in MR images comprises an area of increasing interest, which also presents numerous methodological challenges. Here we describe a new knowledge-based automated method designed to identify several major brain sulci and then to define the frontal lobes by using the identified sulci as landmarks. To identify brain sulci, sulcal images were generated by morphologic operations and then separated into different components based on connectivity analysis. Subsequently, the individual anatomic features were evaluated by using fuzzy membership functions. The crisp decisions, i.e., the identification of sulci, were made by taking the maximum of the summation of all the membership functions. The identification was designed in a hierarchical order. The longitudinal fissure was extracted first. The left and right central sulci were then identified based on the left and right hemispheres. Next, the lateral sulci were identified based on the central sulci and hemispheres. Finally, the left and right frontal lobes were defined from the two hemispheres. The method was evaluated by visual inspection, comparison with manual segmentation, and comparison with manually volumetric results in references. The average Jaccard similarities of left and right frontal lobes between the automated and manual segmentation were 0.89 and 0.91, respectively. The average Kappa indices of left and right frontal lobes between the automated and manual segmentation were 0.94 and 0.95, respectively. These results show relatively high accuracy of using this novel method for human frontal lobe identification and segmentation.
