Daniel Flores-Tapia

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 segments. Once the HS segments are identified, the method removes them from the wavelet coefficients at every level and estimate the created gaps by either an autoregressive or moving average model. 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 without hampering the main components of the LS. The results were confirmed both qualitatively by listening to the reconstructed signal and quantitatively by spectral analysis.

Real time breast microwave radar image reconstruction using circular holography: A study of experimental feasibility

PURPOSE The purpose of this paper is to assess the experimental feasibility of a novel breast microwave radar reconstruction approach, circular holography, using realistic experimental datasets recorded using a preclinical experimental setup. The performance of this approach was quantitatively evaluated by calculating the signal to noise ratio, contrast to noise ratio, spatial accuracy, and reconstruction time. METHODS Six datasets were recorded, three corresponding to fatty cases and three containing synthetic dense tissue structures. Five of these datasets contained an 8 mm inclusion that emulated a malignant lesion. The data were acquired from synthetic phantoms that mimic the dielectric properties of breast tissues in the 1-6 GHz range using a custom experimental breast microwave radar system. The spatial accuracy and signal to noise ratio of the reconstructed was calculated for all the reconstructed images. The contrast to noise ratio of the reconstructed images corresponding to the datasets containing fibroglandular tissue regions was determined. This was done to evaluate the ability of the circular holographic method to provide images in which the responses from tumors can be distinguished from adjacent dense tissue structures. The execution time required to form the images was also measured to evaluate the data throughput of the holographic approach. RESULTS For all the reconstructed datasets, the location of the synthetic tumors in the experimental setup was consistent with its position in the reconstructed image. The average spatial error was 2.2 mm, which is less than half the spatial resolution of the data acquisition system. The average signal to noise ratio of the reconstructed images containing an artificial malignant lesion was 8.5 dB, while the average contrast to noise ratio was 6.7 dB. The reconstructed images presented no artifacts. The average execution time of the images formed using the proposed approach was 5 ms, which is six orders of magnitude faster than current state of the art breast microwave radar (BMR) reconstruction algorithms. CONCLUSIONS The results show that circular holography is capable of forming accurate images with signal to noise levels higher than 8 dB in quasi real time. Compared to BMR reconstruction algorithms tested on datasets containing dense tissue structures, the holographic approach generated images of similar spatial accuracy with higher signal to noise ratios and an acceleration factor of one order of magnitude.

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.

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

A BIMODAL RECONSTRUCTION METHOD FOR BREAST CANCER IMAGING

Breast Microwave Radar (BMR) has been proposed as an alternative modality for breast imaging. This technology forms a re∞ectivity map of the breast region by illuminating the scan area using ultra wide band microwave waveforms and recording the re∞ections from the breast structures. Nevertheless, BMR images require to be interpreted by an experienced practitioner since the location and density of the breast region can make the detection of malignant lesions a di-cult task. In this paper, a novel bimodal breast imaging reconstruction method based on the use of BMR and Electrical Impedance Tomography (EIT) is proposed. This technique forms an estimate of the breast region impedance map using its corresponding BMR image. This estimate is used to initialize an EIT reconstruction method based on the monotonicity principle. The proposed method yielded promising results when applied to MRI-derived numeric breast phantoms.

Hybrid microwave tomography technique for breast cancer imaging.

This paper addresses a two-dimensional inverse scattering method with a combination of tomography and radar methods for breast cancer detection. In order to rapidly construct high resolution images displaying the location, size, permittivity and conductivity of malignant tumors inside the body, the collected reflection from the scattered fields present in the scan area is segmented and their associated dielectric property maps are calculated. The dielectric profiles are obtained by using a technique that combines frequency domain finite difference time domain (FD)2TD analysis with genetic algorithm (GA) optimization. The applications of the proposed method can vary from medical imaging to nondestructive testing of materials and structures. The proposed technique yielded promising results when applied to simulated data

Initial classification of breast tumour phantoms using a UWB radar prototype

This study presents the first experimental results using a pre-clinical UWB prototype imaging system for tumour classification based on the shape of tumours. A database of 13 benign and 13 malignant tumours with average diameters between 13 and 40 mm was created using dielectrically-representative tissue-mimicking material. Classification of benign and malignant tumour models of the experimental data was completed with Linear Discriminant Analysis and Quadratic Discriminant Analysis classifiers.

Real time MRI prostate segmentation based on wavelet multiscale products flow tracking

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. Combined Magnetic Resonance Imaging and Spectroscopic Imaging (MRI/MRSI) techniques have became a reliable tool for early stage prostate cancer detection. Nevertheless, their performance is strongly affected by the determination of the region of interest (ROI) prior to data acquisition process. The process of executing prostate MRI/MRSI techniques can be significantly enhanced by segmenting the whole prostate. A novel method for segmentation of the prostate in MRI datasets is presented. 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 clinical datasets.