Gabriel Thomas

Heart sound cancellation from lung sound recordings using adaptive threshold and 2D interpolation in time-frequency domain

During lung sound recordings, an incessant noise source occurs due to heart sounds. The heart sound interference on lung sounds is significant especially at low flow rates. In this paper a new heart noise (HN) cancellation method is presented. This algorithm uses an image processing technique to detect HN segments in the spectrogram of the recorded lung sound signal. Afterwards the algorithm removes those segments and estimates the missing data employing a 2D interpolation in the time-frequency domain and finally reconstructs the signal in the time domain. The results show that the proposed method successfully cancels HN from lung sound signals while preserving the original fundamental components of the lung sound signal. The computational load and the speed of the proposed method were found to be much more efficient than other HN cancellation methods such as adaptive filtering.

Chaotic signals for wideband radar imaging

We explore the characteristics of chaos for wideband radar imaging. Chaos can be generated via non-linear functions that produce statistically independent samples with invariant probability density functions. By feeding this type of chaos to the input of a voltage-controlled oscillator, a stochastic frequency modulated signal with fractal features is generated. The FM signal is an ergodic and stationary process with initial random phase. The power spectral density of such signal is typically broadband. We show that the time autocorrelation associated with the FM signal provides high range resolution for zero Doppler and dies out rapidly for increasing Doppler shifts. Furthermore, we show that a set of realizations of the signal can be processed into a set of ambiguity surfaces that when averaged yield a low self-noise pedestal.

Histogram Specification: A Fast and Flexible Method to Process Digital Images

Histogram specification has been successfully used in digital image processing over the years. Mainly used as an image enhancement technique, methods such as histogram equalization (HE) can yield good contrast with almost no effort in terms of inputs to the algorithm or the computational time required. More elaborate histograms can take on problems faced by HE at the expense of having to define the final histograms in innovative ways that may require some extra processing time but are nevertheless fast enough to be considered for real-time applications. This paper proposes a new technique for specifying a histogram to enhance the image contrast. To further evidence our faith on histogram specification techniques, we also discuss methods to modify images, e.g., to help segmentation approaches. Thus, as advocates of these techniques, we would like to emphasize the flexibility of this image processing approach to do more than enhancing images.

Semi automatic MRI prostate segmentation based on wavelet multiscale products

Currently, prostate cancer is the third leading cause of cancer-related deaths among men in North America. As with many others types of cancer, early detection and treatment greatly increases the patients chance of survival. MRI prostate segmentation allows clinical personnel to design an accurate treatment plan. A novel method for MRI prostate imagery segmentation is proposed in this paper. This method exploits the different behavior presented by signal singularities and noise in the wavelet domain in order to accurately detect the borders around the prostate. The prostate contour is then traced by using a set of spatially variant rules that are based on prior knowledge about the general shape of the prostate. The proposed method yielded promising results when applied to real data.

A Wavefront Reconstruction Method for 3-D Cylindrical Subsurface Radar Imaging

In recent years, the use of radar technology has been proposed in a wide range of subsurface imaging applications. Traditionally, linear scan trajectories are used to acquire data in most subsurface radar applications. However, novel applications, such as breast microwave imaging and wood inspection, require the use of nonlinear scan trajectories in order to adjust to the geometry of the scanned area. This paper proposes a novel reconstruction algorithm for subsurface radar data acquired along cylindrical scan trajectories. The spectrum of the collected data is processed in order to locate the spatial origin of the target reflections and remove the spreading of the target reflections which results from the different signal travel times along the scan trajectory. The proposed algorithm was successfully tested using experimental data collected from phantoms that mimic high contrast subsurface radar scenarios, yielding promising results. Practical considerations such as spatial resolution and sampling constraints are discussed and illustrated as well.

Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation

Prostate cancer is one of the leading causes of death in men. Accurate segmentation of prostate magnetic resonance imagery allows for the maximum volume of the prostate to be considered in diagnosis, using magnetic resonance spectroscopy, and in treatment using intensity modulated radiotherapy. In this work a semi-automatic method which segments the prostate on magnetic resonance images is presented. This algorithm evaluates the curve of the prostate in an orientation based frame work. Having computed the edge direction of each individual pixel in the region surrounding the prostate, and considering the location of four points of this region that have been previously selected by the user, a statistical average mask of size 3times3 follows the direction of a channel of low intensity pixels around the prostate using prior knowledge of the shape of the object. The initial results show promising results when comparing this method to the segmentation done by an expert

Using a priori Information for Regularization in Breast Microwave Image Reconstruction

Regularization methods are used in microwave image reconstruction problems, which are ill-posed. Traditional regularization methods are usually problem-independent and do not take advantage of a priori information specific to any particular imaging application. In this paper, a novel problem-dependent regularization approach is introduced for the application of breast imaging. A real genetic algorithm (RGA) minimizes a cost function that is the error between the recorded and the simulated data. At each iteration of the RGA, a priori information about the shape of the breast profiles is used by a neural network classifier to reject the solutions that cannot be a map of the dielectric properties of a breast profile. The algorithm was tested against four realistic numerical breast phantoms including a mostly fatty, a scattered fibroglandular, a heterogeneously dense, and a very dense sample. The tests were also repeated where a 4 mm × 4 mm tumor was inserted in the fibroglandular tissue in each of the four breast types. The results show the effectiveness of the proposed approach, which to the best of our knowledge has the highest resolution amongst the evolutionary algorithms used for the inversion of realistic numerical breast phantoms.

Radar signal design using chaotic signals

The use of chaotic signals in radar imaging applications present particular advantages as they behave like pseudo noise, have a wide band, and are easy to generate. A chaotic frequency modulated (FM) sine wave is an example of a chaotic signal that can yield higher transmitted mean power when peak-power limited transmitters are used. Unlike the random FM signal, the behavior of chaotic FM signals is not fully understood. In this paper, two approaches for analyzing the spectrum of chaotic FM signals are discussed. The first approach approximates the chaotic signal with noise and the second one, deals with the condition for the chaotic signal to remain chaotic after frequency modulation and consequently have a wide band spectrum.

Heart Sound Cancellation Based on Multiscale Products and Linear Prediction

This paper presents a novel method for Heart Sound (HS) cancellation from Lung Sound (LS) records. The method uses the multiscale product of the wavelet coefficients of the original signal to detect HS-included segments. Once the HS segments are identified, the method removes them from the wavelet coefficients at every level and estimates the created gaps by using a set of linear prediction filters. It is shown that if the segment to be predicted is stationary, a final record with no audible artifacts such as clicks can be reconstructed using this approach. The results were promising for HS removal from LS records and showed no hampering of the main components of the LS. The results were confirmed both qualitatively by listening to the reconstructed signal and quantitatively by spectral analysis

Breast Tumor Microwave Simulator Based on a Radar Signal Model

Breast cancer incidence in women has increased from one in twenty in 1960 to one in eight today. Although advances have improved the likelihood of early detection, current breast imaging modalities still have limitations. In recent years, microwave imaging has shown its potential as an alternative approach for breast cancer detection. The principle behind this approach is the detection of differences in electrical characteristics between normal and malignant breast tissues in the microwave frequency range. A novel simulation technique for radar breast microwave imagery is proposed in this paper. Dispersive effects in the propagation medium and different antenna radiation pattern sizes are included in the simulation model. The proposed method produced accurate results when compared to real data collected from a phantom that mimics the average differences in dielectric properties from skin, breast, and malignant tissue