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Abstract
In recent years, multi-resolution techniques have become increasingly popular in the image processing community. New techniques have been developed with applications ranging from edge detection, texture recognition, image registration, multi-resolution features for image classification and more. The central focus of this two-part thesis is the multi-resolution analysis of images. In the first part, we used multi-resolution approaches to help with the classification of a set of protein crystal images. In the second, similar approaches were used to help register a set of 3D image volumes that would otherwise be computationally prohibitive without leveraging multi-resolution techniques.
Specifically, the first part of this work proposes a classification framework that is being developed in collaboration with NorthEast Structural Genomics Consoritum (NESG) to assist in the automated screening of protein crystal images. Several groups have previously proposed automated algorithms to expedite such analysis. However, none of the classifiers described in the literature are sufficiently accurate or fast enough to be practical in a structural genomics production pipeline. The proposed classification algorithm uses random window sampling of the regions of interest to then compute several texture and multi-resolution image descriptor features that are subsequently processed through a random forest classifier. The resulting binary classifier exceeds 90% in sensitivity and 94% in specificity. Furthermore, the classifier is able to process each image with off-the-shelf computer components at approximately 7 seconds for each image, a speed that makes this algorithm usable in high throughput settings.
The second part of this work proposes a 3D image registration algorithm to register regions of emphysema as quantified by densitometry on lung CT with MR lung volumes. The ability to register quantitatively-determined regions of emphysema with perfusion MRI will allow for further exploration of the pathophysiology of Chronic Obstructive Pulmonary Disorder (COPD). The registration method involves the registration of CT volumes at different levels of inspiration (total lung capacity to functional residual capacity [FRC]) followed by another registration between FRC-CT and FRC-MR volume pairs. We propose a registration method based on a combination of cubic b-spline registrations that is relatively quick (~4.5 minutes) and accurate (~6.3%). The methods presented in this work are being used to explore the relationships between regions of emphysema and their pulmonary microvascular blood flow during longitudinal progression of COPD.
