Segmentation of Substantia Nigra for the Automated Characterization of Parkinson's Disease

Segmentation of smaller brainstem nuclei like substantia nigra (SN) is one of the first stage in computer-aided diagnosis to investigate the Parkinson's disease (PD) characteristics and progression. PD patients generally have smaller SN in comparison to healthy individuals. There are very few automated methods proposed for SN segmentation and most of them require quite a few number of reference images for increasing the accuracy. Therefore, we propose an improved algorithm for the segmentation of SN using level set method and dual-tree complex wavelet transform (DT-CWT). The proposed level set method uses the local image information and maintains the regularity of the level set function for accurate computation, thus precluding the expensive re-initialization process. DT-CWT is used to solve the problem of over-segmentation, also resulting in holes, and to smooth the jagged outputs generated by the level set method. The experimental results suggest that the proposed algorithm is able to segment SN and is closer to manual delineation.