Background: Recent research has shown that there is a strong correlation between the functional properties of a neuron and its morphologic structure. Current morphologic analyses typically involve a significant component of computer-assisted manual labor, which is very time-consuming and is susceptible to operator bias. The existing semi-automatic approaches largely reduce user efforts. However, some manual interventions, such as setting a global threshold for segmentation, are still needed during image processing. Methods: We present an automated approach, which can greatly help neurobiologists obtain quantitative morphological information about a neuron and its spines. The automation includes an adaptive thresholding method, which can yield better segment results than the prevalent global thresholding method. It also introduces an efficient backbone extraction method, a SNR based, detached spine component detection method, and an attached spine component detection method based on the estimation of local dendrite morphology. Results: The morphology information obtained both manually and automatically are compared in detail. Using the Kolmogov-Smirnov test, we find a 99.13% probability that the dendrite length distributions are the same for the automatic and manual processing methods. The spine detection results are also compared with other existing semi-automatic approaches. The comparison results show that our approach has 33% fewer false positives and 77% fewer false negatives on average. Conclusions: Because the proposed detection algorithm requires less user input and performs better than existing algorithms, our approach can quickly and accurately process neuron images without user intervention.
- Adaptive thresholding
- Automatic dendritic spine detection
ASJC Scopus subject areas