Directional multiscale representations and applications in digital neuron reconstruction
Recent advances in the field of multiscale representations have spurred the emergence of a new generation of powerful techniques for the efficient analysis of images and other multidimensional data. These novel techniques enable the quantification of essential geometric characteristics in complex imaging data resulting in improved algorithms for image restoration, feature extraction and classification. We discuss the application of these ideas in neuroscience imaging and describe a novel method for the accurate and efficient identification of cellular bodies of neurons in multicellular images. This method is instrumental to the design of a novel algorithm for neuronal tracing.
Fluorescent microscopy, Multiscale analysis, Neuron profiling, Neuron reconstruction, Sparse representations, Wavelets
Kayasandik, Cihan, Kanghui Guo, and Demetrio Labate. "Directional multiscale representations and applications in digital neuron reconstruction." Journal of computational and applied mathematics 349 (2019): 482-493.
Journal of Computational and Applied Mathematics