The branches extending from the cell body of neurons, the dendrites, receive more than 90% of the synaptic contacts made into that neuron. In many neurons of the mammalian brain, excitatory synapses involve specialized structures called dendritic spines that protrude from the dendrites and contain the molecules and organelles involved in the postsynaptic processing of the synaptic information. Neuron morphology, as captured in part by the structure of these spines, is illustrative of neuronal function and can be instrumental in better understanding the dysfunction seen in neurodegenerative conditions such as Alzheimer's and Parkinson's disease. Hence researchers have shown great interest in quantitatively studying dendritic spine morphology and density both statically and as a function of time. Such studies are typically carried out through the analysis of data collected from a range of microscopy modalities including confocal laser scanning microscopy (CLSM) and two-photon laser scanning microscopy (2PLSM).;Due to the size and complexity of these data sets, manually analyzing the morphological changes of dendritic spines is very time consuming. In the thesis, we describe robust, automated approaches for dendritic spine detection and measurement that are especially suitable to the batch processing of large neuronal image data sets. Our work is roughly divided into three related components. First, we focus on an image processing pipeline we have developed for the neuroinformatics processing system released from our lab called Neuron Image Quantitator (NeuronIQ), an integrated system for automatic dendrite spine detection, quantification, and analysis. Second, to further improve detection results and solve a collection of related "hard problems" (such as disconnected spine segmentation) faced by existing automatic or semi-automatic methods, a post-processing segmentation algorithm based on a Maximum a Posteriori-orientated Markov random field (MAP-OMRF) is discussed in detail. Finally, we will present an efficient particle filter-based algorithm that is capable of tracking morphological changes in the spines over time. Possible future topics will be discussed at the end of the thesis.
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