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Active research in the area of 3-D neurite tracing has predominantly focused on single sections. Ultimately, however, neurobiologists desire to study the long range connectivity of the brain, which requires tracing axons across multiple serially-cut sections. Registration of axonal sections is challenging due to several factors, such as sparseness of the axons and complications of the sectioning process,...
Complete mapping of neuronal networks requires data acquisition at synaptic resolution with canonical coverage of tissues and robust neuronal classification. Transmission electron microscopy (TEM) remains the optimal tool for network mapping. However, capturing high resolution, large, serial section TEM (ssTEM) image volumes is complicated by the need to precisely mosaic distorted image tiles and...
To better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected...
Neurobiologists are collecting large amounts of electron microscopy image data to gain a better understanding of neuron organization in the central nervous system. Image analysis plays an important role in extracting the connectivity present in these images; however, due to the large size of these datasets, manual analysis is essentially impractical. Automated analysis, however, is challenging because...
In our study of regional climate modeling and simulation, we frequently encounter vector fields that are crowded with large numbers of critical points. A critical point in a flow is where the vector field vanishes. While these critical points accurately reflect the topology of the vector fields, in our study only a subset of them is worth further investigation. We present a filtering technique based...
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