Functional Magnetic Resonance Imaging (fMRI) is a powerful technique for studying the working of the human brain. This overall goals of the project are to devlop a novel method for the analysis of fMRI data in order to discover the activation of a network of regions involving most likely the hippocampus, parietal cortex and cerebellum as a person is navigating in a virtual environment. Spatially sensitive voxels are extracted by selecting voxels that have high mutual information. Each of these extracted voxels is then used to create a response curve for the stimulus of interest, in this case spatial location. Following the voxel extraction stage, the set of extracted voxel time series would be treated as a population and used to predict the location of the subject at any randomly selected time in the experiment. The population of voxels essentially "votes" with their current activity. The approach used for prediction is the Bayesian reconstruction method. The ability to predict the location of a subject in the virtual environment based on brain signals will be useful in developing a physiological understanding of spatial cognition in virtual environments.
The goal of this work is to develop a system that can semi-automate the detection of multicolored foreground objects in digitized color images that also contain complex and very noisy backgrounds. Although considered a general problem of color image segmentation, our application is microbiology where various colored stains are used to reveal information on the microbes without cultivation. Instead of providing a simple threshold, the proposed system offers an interactive environment whereby the user chooses multiple sample points to define the range of color pixels comprising the foreground microbes of interest. The system then uses the color and spatial distances of these target points to segment the microbes from the confusing background of pixels whose RGB values lie outside the newly defined range and finally finds each cell's boundary using region-growing and mathematical morphology. Some other image processing methods are also applied to enhance the resultant image containing the colored microbes against a noise-free background. The prototype performs with 98% accuracy on a test set compared to ground truth data. The system described here will have many applications in image processing and analysis where one needs to segment typical pixel regions of similar but non-identical colors.