An automatic level set method for hippocampus segmentation in MR images

Abstract

ABSTRACT Hippocampus segmentation in MR images is beneficial for the diagnosis of many diseases and pathologies such as Alzheimer’s disease. Manual segmentation of the hippocampus is highly time-consuming and has low reproducibility; however, automated methods have introduced substantial gains in this regard. In this study, we used a novel level-set method for hippocampus segmentation in combination with the SBGFRLS (Selective Binary and Gaussian Filtering Regularised Level Set) and LAC (Localising Region-Based Active Contours) algorithms. The proposed method avoided the algorithms which required a large database and instead used a more complex level set approach to obtain comparable accuracy. This method was applied to a set of 36 MRI scans provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), using the Harmonised Hippocampal Protocol (HarP) as the gold standard. In addition, the results were compared with the outputs of the Freesurfer software package. In regards to the similarity indices, the results of our algorithm (mean Dice = 0.847) were more comparable with the gold standard compared to those of Freesurfer. Classification results for AD vs control and MCI vs control showed a high degree of accuracy (91% and 75%, respectively). Therefore, this method can be an option for accurate and robust segmentation of the hippocampus.