Todd Reed

Heart sound analysis for symptom detection and computer-aided diagnosis

Heart auscultation (the interpretation by a physician of heart sounds) is a fundamental component of cardiac diagnosis. It is, however, a difficult skill to acquire. In this work, we develop a simple model for the production of heart sounds, and demonstrate its utility in identifying features useful in diagnosis. We then present a prototype system intended to aid in heart sound analysis. Based on a wavelet decomposition of the sounds and a neural network-based classifier, heart sounds are associated with likely underlying pathologies. Preliminary results promise a system that is both accurate and robust, while remaining simple enough to be implemented at low cost.

Motion Estimation in the 3-D Gabor Domain

Motion estimation methods can be broadly classified as being spatiotemporal or frequency domain in nature. The Gabor representation is an analysis framework providing localized frequency information. When applied to image sequences, the 3-D Gabor representation displays spatiotemporal/spatiotemporal-frequency (st/stf) information, enabling the application of robust frequency domain methods with adjustable spatiotemporal resolution. In this work, the 3-D Gabor representation is applied to motion analysis. We demonstrate that piecewise uniform translational motion can be estimated by using a uniform translation motion model in the st/stf domain. The resulting motion estimation method exhibits both good spatiotemporal resolution and substantial noise resistance compared to existing spatiotemporal methods. To form the basis of this model, we derive the signature of the translational motion in the 3-D Gabor domain. Finally, to obtain higher spatiotemporal resolution for more complex motions, a dense motion field estimation method is developed to find a motion estimate for every pixel in the sequence.

A bond percolation-based model for image segmentation

This work presents a novel bond percolation-based approach to determine the clique potential parameters of a Gibbs-Markov model as a function of local characteristics of the underlying image. Using the renormalization group transformation, a multiscale description of the clique potential parameters is formed and used to obtain multiresolution image segmentation.

Detection and estimation of rotational motions using the 3-D Gabor representation

In this paper, detection and estimation of rotational motion in the 3-D Gabor domain are investigated. By extending frequency domain methods to the joint spatiotemporal/spatio-temporal-frequency (st/stf) analysis framework, we propose a technique to estimate multiple localized rotational motions in image sequences. Based on a rotational motion model derived in the 3-D Gabor domain, an algorithm for estimating localized uniform rotational motions is constructed and discussed.

Examining the effects of basis function truncation in the DGT

The derivative of Gaussian transform is a spatial/spatial frequency representation of an image that has been used in image and video compression algorithms. Both the forward and inverse transforms require O(N/sup 3/) operations. We consider truncating the length N basis functions to produce a sparse basis matrix and thus to reduce the computational load of the reconstruction. We examine the effects of truncation on the basis function energy and evaluate the change in a number of basis quality measures as the basis function length is decreased.