Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

ABSTRACT: High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We com

SUBMITTER: Kandaswamy U 

PROVIDER: S-EPMC3721977 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature