A Better Looking Brain: Image Pre-Processing Approaches for fMRI Data

Researchers in the field of functional neuroimaging have faced a long standing problem in pre-processing low spatial resolution data without losing meaningful details within. Commonly, the brain function is recorded by a technique known as echo-planar imaging that represents the measure of blood flow (BOLD signal) through a particular location in the brain as an array of intensity values changing over time. This approach to record a movie of blood flow in the brain is known as fMRI. The neural activity is then studied from the temporal correlation patterns existing within the fMRI time series. However, the resulting images are noisy and contain low spatial detail, thus making it imperative to pre-process them appropriately to derive meaningful activation patterns. Two of the several standard preprocessing steps employed just before the analysis stage are denoising and normalization. Fundamentally, it is difficult to perfectly remove noise from an image without making assumptions about signal and noise distributions. A convenient and commonly used alternative is to smooth the image with a Gaussian filter, but this method suffers from various obvious drawbacks, primarily loss of spatial detail. A greater challenge arises when we attempt to derive average activation patterns from fMRI images acquired from a group of individuals. The brain of one individual differs from others in a structural sense as well as in a functional sense. Commonly, the inter-individual differences in anatomical structures are compensated for by co-registering each subject\u27s data to a common normalization space, known as spatial normalization. However, there are no existing methods to compensate for the differences in functional organization of the brain. This work presents first steps towards data-driven robust algorithms for fMRI image denoising and multi-subject image normalization by utilizing inherent information within fMRI data. In addition, a new validation approach based on spatial shape of the activation regions is presented to quantify the effects of preprocessing and also as a tool to record the differences in activation patterns between individual subjects or within two groups such as healthy controls and patients with mental illness. Qualititative and quantitative results of the proposed framework compare favorably against existing and widely used model-driven approaches such as Gaussian smoothing and structure-based spatial normalization. This work is intended to provide neuroscience researchers tools to derive more meaningful activation patterns to accurately identify imaging biomarkers for various neurodevelopmental diseases and also maximize the specificity of a diagnosis

Automatic multi-resolution spatio-frequency mottle metric (sfmm) for evaluation of macrouniformity

Evaluation of mottle is an area of on-going research in print quality assessment. We propose an unsupervised evaluation technique and a metric that measures mottle in a hard-copy laser print. The proposed algorithm uses a scanned image to quantify the low frequency variation or mottle in what is supposed to be a uniform field. `Banding\u27 and `Streaking\u27 effects are explicitly ignored and the proposed algorithm scales the test targets from Flat print (Good) to Noisy print (Bad) based on mottle only. The evaluation procedure is modeled as feature computation in different combinations of spatial, frequency and wavelet domains. The model is primarily independent of the nature of the input test target, i.e. whether it is chromatic or achromatic. The algorithm adapts accordingly and provides a mottle metric for any test target. The evaluation process is done using three major modules: (1) Pre-processing Stage, which includes acquisition of the test target and preparing it for processing; (2) Spatio-frequency Parameter Estimation where different features characterizing mottle are calculated in spatial and frequency domains; (3) Invalid Feature Removal Stage, where the invalid or insignificant features (in context to mottle) are eliminated and the dataset is ranked relatively. The algorithm was demonstrated successfully on a collection of 60 K-Only printed images spread over 2 datasets printed on 3 different faulty printers and 4 different media Also, it was tested on 5 color targets for the color version of the algorithm printed using 2 different printers and 5 different media, provided by Hewlett Packard Company

An experimental study of bubbles and droplets rising in a nematic liquid crystal

Liquid crystals are nature's beautiful examples of complex materials which are fundamentally fascinating. Their unusual properties have intrigued researchers from a wide variety fields including biologists, engineers, and even cosmologists. This thesis focuses on the dynamics of topological defects occurring near micro-droplets and micro-bubbles as they rise through an aligned nematic liquid crystal. The experiments were conducted in a fabricated flow-cell, and the observations were made using polarized light microscopy with the help of a motion control system. The results settle a controversy in the literature regarding the effect of hydrodynamic flow on the motion of defects by providing direct evidence of downstream convection of a Saturn ring defect and its transformation to a hyperbolic point defect. The point defect is convected further in the wake of the drop or bubble as the rising velocity increases. In equilibrium, both defect configurations may persist for long times. But the point defect sometimes spontaneously opens into a Saturn ring, indicating the latter as the globally stable configuration for the conditions used. A quantitative analysis of the rise velocities versus the location of defects yields graphs which are consistent with recent theoretical predictions. Besides these, we also observe interesting multiple drop and bubble interactions leading to the phenomenon of self-assembly and distorted defect structures.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat