LAUSR

Most of neuroimaging applications tend to still rely on expert knowledge in determining anatomies of the brain. For example in Parkinson’s disease surgery, detection of the anterior commissure (AC) and posterior commissure (PC) are still done manually by doctors. Previously, various methods have been developed related to the automatic detection of AC and PC. However, the majority of previously methods have several drawbacks, such as only compatible on T1-W or T2-W, only compatible for data with the same matrix size, and requires a time-consuming training process. This study proposes a new strategy by combining a multilevel thresholding and morphological relationships approach for automatic detection of AC and PC. The process divided into 4 main stages: preprocessing, multilevel thresholding, segmentation, and detection of AC and PC. The segmentation is performed on several anatomies of the brain including corpus callosum, fornix, and colliculus. From the experiment, it can be concluded that the use of multilevel thresholding and morphological relationship was successfully detecting AC and PC with the mean error were 1.02 mm and 1.06 mm, respectively. The proposed method can perform an automatic detection of AC and PC with simply algorithm, does not require a large of diverse data sets for the training process, without training process that takes up time, and reliable on the diversity of MRI since it is compatible for T1-W and T2-W with various matrix sizes of 256 x 256 and 512 x 512 pixels which cannot be handled by previous researches.