Aboofazeli, Mohammad

The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchi's approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features. [ABSTRACT FROM AUTHOR]

PURPOSE: A ground-truth validation platform was developed to provide spatial correlation between ultrasound (US), temperature measurements and histopathology images to validate US based thermal ablation monitoring methods. <p>METHOD: The test-bed apparatus consists of a container box with integrated fiducial lines. Tissue samples are suspended within the box using agar gel as the fixation medium. Following US imaging, the gel block is sliced and pathology images are acquired. Interactive software segments the fiducials as well as structures of interest in the pathology and US images. The software reconstructs the regions in 3D space and performs analysis and comparison of the features identified from both imaging modalities. <p>RESULTS: The apparatus and software were constructed to meet technical requirements. Tissue samples were contoured, reconstructed and registered in the common coordinate system of fiducials. There was agreement between the sample shapes, but systematic shift of several millimeters was found between histopathology and US. This indicates that during pathology slicing shear forces tend to dislocate the fiducial lines. Softer fiducial lines and harder gel material can eliminate this problem. <p>CONCLUSION: Viability of concept was presented. Despite our straightforward approach, further experimental work is required to optimize all materials and customize software. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for tissue characterization using wavelet transform of ultrasound radio frequency (RF) echo signals. We propose the use of multiscale products of wavelet transform sequences of RF echoes to estimate the scatterer distribution in the tissue. The proposed method is based on the fact that when emitted ultrasound beams interact with scatterers in the tissue, backscattered beams contain singularities corresponding to the location of the scatterers. The singularities will exist in multiple scales of wavelet sequences of the echo signals. Therefore, peaks of wavelet transform multiscale products correspond to the location of scatterers. Estimation of scatterer spacing can be used for tissue characterization. The efficacy of the proposed method was validated in RF echo signals of in-vitro human prostate to characterize normal and cancerous tissue. The results confirm that wavelet transform multiscale products of RF echo signals contain tissue typing information that can be used as an effective tool to differentiate normal and cancerous prostate tissue.

Ultrasound image guided needle insertion is the method of choice for a wide variety of medical diagnostic and therapeutic procedures. When flexible needles are inserted in soft tissue, these needles generally follow a curved path. Segmenting the trajectory of the needles in ultrasound images will facilitate guiding them within the tissue. In this paper, a novel algorithm for curved needle segmentation in three-dimensional (3D) ultrasound images is presented. The algorithm is based on the projection of a filtered 3D image onto a two-dimensional (2D) image. Detection of the needle in the resulting 2D image determines a surface on which the needle is located. The needle is then segmented on the surface. The proposed technique is able to detect needles without any previous assumption about the needle shape, or any a priori knowledge about the needle insertion axis line.

In this study, average power of tracheal sound (P ave ) was used to estimate flow by parametric method as well as adaptive filters as a nonparametric method. Based on some preliminary studies, an exponential model was used for describing the relationship between flow and P ave for parametric method. It was assumed that flow signal of at least one breath from each target flow is available for calibration. The error for flow estimation with parametric method, was found to be 9plusmn3% and 10plusmn4% for inspiration and expiration, respectively. Considering nonparametric method, the estimation error was the least for the third order adaptive filter using the average power of the tracheal sound (dB), which was 10plusmn3 % and 11plusmn4 % for inspiration and expiration, respectively.

The paper presents a quantitative analysis of swallowing sounds in normal and dysphagic subjects based on nonlinear dynamics metric tools. In addition, an automated method is proposed to identify patients at risk of dysphagia. Multidimensional phase space representation of the swallowing sound was reconstructed using Takens method of delays. Rosenstein and false nearest neighbor (FNN) methods were employed to evaluate the optimum time delay and proper embedding dimension, respectively. A Grassberger-Procaccia algorithm was utilized to calculate the correlation dimension as a measure of the complexity of the reconstructed attractor. The analysis demonstrated the low-dimensional dynamic characteristics of normal and dysphagic swallowing sounds. The optimum time delay and correlation dimension of the opening and transmission phases of swallowing sounds were used as features for a 3-nearest neighbor classifier to identify individuals at risk of dysphagia. The method was applied to tracheal sound recordings of 15 healthy subjects and 11 patients with some degree of dysphagia. The algorithm was able to classify 83% of swallows correctly. Finally, a screening algorithm was used which correctly classified 24 out of 26 subjects. This study suggests that nonlinear analysis is a promising tool for quantitative analysis of swallowing sounds and swallowing disorders.

Digital image watermarking techniques have been proposed to prevent unauthorized distribution of multimedia data. A digital watermark encodes the owner's license information and embeds it into the image. Several discrete wavelet transform (DWT) based techniques are used for watermarking. In this paper, a watermarking scheme is proposed in which the image is decomposed into wavelet coefficients and a visual recognizable logo is embedded in the wavelet coefficients. Wavelet coefficients corresponding to the points located in a neighborhood that have maximum entropy are proposed for embedding the watermark. This method embeds the maximum amount of watermark while the watermark is imperceptible. Watermarking techniques must be robust to some attacks such as smoothing, sharpening and compression. These maximum entropy areas can survive a variety of attacks and can be used as reference points for watermark embedding. The experimental results confirmed that the technique is robust to a variety of attacks.

Several metric tools for quantative analysis of scalar time series have been developed using the theory of nonlinear dynamics. The goal of this work was to study the characteristics of swallowing sound using these metric tools. Takens method of delays was used to reconstruct multidimensional state space representation of the swallowing sounds of 6 healthy subjects (ages 13-30 years, 3 males) being fed thin and thick liquid textures. The optimum time delay for different subjects varied from 3 to 9 samples. False nearest neighbors method was used to obtain proper embedding dimension. The correlation dimension was calculated based on Grassberger-Procaccia algorithm. The results suggest that swallowing sound is well characterized by a small number of dimensions. The largest Lyapunov exponent was also estimated to evaluate the presence of chaos. As the largest Lyapunov exponent for some cases was negative, it may be concluded that swallowing sound is not necessarily a chaotic process.

The goal of this study was to develop an automated and objective method to separate swallowing sounds from breath sounds. Swallowing sound detection can be utilized as part of a system for swallowing mechanism assessment and diagnosis of swallowing dysfunction (dysphagia) by acoustical means. In this study, an algorithm based on multilayer feed forward neural networks is proposed for decomposition of tracheal sound into swallowing and respiratory segments. Among many features examined, root-mean-square of the signal, the average power of the signal over 150-450 Hz and waveform fractal dimension were selected features applied to the neural network as inputs. Findings from previous studies about temporal and durational patterns of swallowing and respiration were used in a smart algorithm for further identification of the swallow and breath segments. The proposed method was applied to 18 tracheal sound recordings of 7 healthy subjects (ages 13-30 years, 4 males). The results were validated manually by visual inspection using airflow measurement and spectrogram of the sounds and auditory means. The algorithm was able to detect 91.7% of swallows correctly. The average of missed swallows and average of false detection were 8.3% and 9.5%, respectively. With additional preprocessing and post processing, the proposed method may be used for automated extraction of swallowing sounds from breath sounds in healthy and dysphagic individuals.

This paper presents an objective method for analysis of temporal pattern of swallowing mechanism based on analysis of swallowing sounds and submental surface electromyogram (EMG). In this study, swallowing sound signal and submental EMG of 12 healthy subjects were recorded. Swallowing sound signals were divided into 25 millisecond segments each of which was represented by waveform fractal dimension (WED). Temporal pattern of swallowing sound signal was identified based on hidden Markov modeling (HMM) of the WED sequences. Submental muscle contraction was marked by thresholding the RMS values of the EMG signals. Duration of the swallowing sound phases, duration of the submental muscle contraction, and time difference between the onset of submental muscle contraction and the opening of cricopharynx were calculated. Experimental results suggest that the proposed method is efficient in the study of temporal pattern of swallowing mechanism and can provide an objective and accurate approach for swallowing mechanism analysis.