Automated nuclei clump decomposition for image analysis in neuronal cell fluorescent microscopy
Automated clump decomposition is crucial for the analysis of neuronal cell fluorescent microscopic images due to inevitable nuclei and cell clumping. Existing techniques are not satisfactory in terms of accuracy and efficiency. In the current work, we propose a new method to automatically decompose nuclei clumps, using multiple visual cues from seeds, skeletons, and object boundaries and nuclei shape models to deliver robust and efficient analysis. The use of shape models enhances its robustness in handling complicated clumps against those bottom-up approaches without prior knowledge. We adopt a strategy of model verification in allowable local shape changes to improve the computational efficiency over template matching. Validation experiments in the analysis of 50 images containing over 2000 nuclei have demonstrated accuracy improvements over existing techniques.