Kyu-hong Kim

A fast minimum variance beamforming method using principal component analysis

Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming.

Dual channel noise reduction method using phase difference-based spectral amplitude estimation

We present a dual-channel noise reduction method for small mobile devices. Our method incorporates phase difference between channels into the conventional MMSE spectral amplitude estimator. It is possible to suppress unwanted directional noise signals, whose incident direction is different from that of the target speech signal. Experimental results show that the proposed method outperformed conventional dual-channel noise reduction methods in SNR and PESQ evaluations in a noisy environment.

Adaptive noise power spectrum estimation for compact dual channel speech enhancement

We present a novel noise power estimation method based on smoothed spectral minima tracking and subtractive blocking matrix for dual-channel speech enhancement. By combining spectral characteristics of the noisy mixture signals with spatial null beam-forming, noise over- and under-estimation problem can be substantially mitigated. The proposed noise estimation tested on the real-life nonstationary noise conditions outperformed other single-channel noise estimation based on minima tracking and dual-channel phase error based enhancement algorithm.

Semi-blind disjoint non-negative matrix factorization for extracting target source from single channel noisy mixture

We present a semi-blind non-negative matrix factorization(NMF) approach to suppress interference noise signals from a single channel mixture signal. By enforcing a disjointness constraint into the NMF error criterion under the semi-blind denoising framework, it is possible to decompose the mixture spectrogram into target and noise components by minimizing overlaps in the time-frequency domain. Experimental results show that the proposed semi-blind disjoint NMF algorithm can significantly suppress non-stationary noise components in the noisy mixture.

Flexible Minimum Variance weights estimation using principal component analysis

Minimum Variance (MV) beamforming has been studied for high resolution ultrasonic imaging. However, it is not easy for the MV beamformer to be implemented into a real time diagnostic system, because it requires too much computation time in calculating covariance matrix inversion. This paper introduces a flexible MV weight estimation that can dynamically reduce the matrix dimension using principal component transform. Principal components are estimated offline from pre-calculated conventional MV weights. It is assumed that all MV weights can be approximated by a linear combination of selected principal vectors. In this paper, flexible MV weight estimation is introduced by deriving a linearly approximated minimum variance criterion with a constraint using Lagrange multiplier. Our method does not directly calculate the MV weights but estimates the weights in the linear combination of the selected principal components. The combinational weights are a function of the inversion of a transformed covariance matrix whose dimension is identical to the number of the selected component vectors. Delay-and-sum (DAS), conventional MV, and flexible MV method were experimented on Field II simulation using point targets and cysts. Our method can reduce the dimension of the covariance matrix down to 2 × 2 while maintaining the good image quality of the minimum variance.

Fast transform-based adaptive beamformer for medical ultrasound imaging

Minimum Variance (MV) beamforming has been studied for high resolution ultrasonic imaging. However, it is not easy for the MV beamformer to be implemented into a real time diagnostic system, because it requires too many computations for covariance matrix inversion. We introduce a transform-based adaptive beamforming algorithm for ultrasound medical imaging where the transformation can reduce the dimension of the covariance matrix in estimating beamformation weights. Moreover, it is shown that beamformation result can be directly calculated in the transformed domain. Experimental results indicate that our beamforming method shows better resolution and contrast under a real phantom and in-vivo environment.

Fast non-blind deconvolution based on 2D point spread function database for real-time ultrasound imaging

In the ultrasound medical imaging system, blurring which occurs after passing through ultrasound scanner system, represents Point Spread Function (PSF) that describes the response of the ultrasound imaging system to a point source distribution. So, de-blurring can be achieved by de-convolving the images with an estimated of PSF. However, it is hard to attain an accurate estimation of PSF due to the unknown properties of the tissues of the human body through the ultrasound signal propagates. In addition to, the complexity is very high in order to estimate point spread function and de-convolve the ultrasound image with estimated PSF for real-time implementation of ultrasound imaging. Therefore, conventional methods of ultrasound image restoration are based on a simple 1D PSF estimation [8] that axial direction only by restoring the performance improvement is not in the direction of Lateral. And, in case of 2D PSF estimation, PSF estimation and restoration of the high complexity is not being widely used. In this paper, we proposed new method for selection of the 2D PSF (estimated PSF of the average speed sound and depth) simultaneously with performing fast non-blind 2D de-convolution in the ultrasound imaging system. Our algorithm works on the beam-formed uncompressed radio-frequency data, with pre-measured and estimated 2D PSFs database from actual probe used. In the 2d PSF database, there are pre-measured and estimated 2D PSFs that classified the each different depth (about 5 different depths) and speed of sound (about 1450 or 1540m/s). Using a minimum variance and simple Weiner filter method, we present a novel way to select the optimal 2D PSF in pre-measured and estimated 2D PSFs database that acquired from the actual transducer being used. For de-convolution part with the chosen PSF, we focused on the low complexity issue. So, we are using the Weiner Filter and fast de-convolution technique using hyper-Laplacian priors [11], [12] which is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors. Then, in order to prevent discontinuities between the differently restored each depth image regions, we use the piecewise linear interpolation on overlapping regions. We have tested our algorithm with vera-sonic system and commercial ultrasound scanner (Philips C4-2), in known speed of sound phantoms and unknown speeds in vivo scans. We have applied a non-blind de-convolution with 2D PSFs database for ultrasound imaging system. Using the real PSF from actual transducer being used, our algorithm produces a better restoration of ultrasound image than de-convolution by simulated PSF, and has low complexity for real-time ultrasound imaging. This method is robust and easy to implement. This method may be a realistic candidate for real-time implementation.

An experimental study on coded excitation in CMUT arrays to utilize Simultaneous Transmission Multiple-zone Focusing method with frequency divided sub-band chirps

The frequency bandwidth of CMUTs (capacitive micromachined ultrasonic transducers) is known as relatively broader than that of other ultrasonic transducers. To utilize the wide bandwidth characteristic of the CMUT arrays, in this paper, we report on coded excitation techniques in the CMUT array. Through simulations, STMF (Simultaneous Transmit Multiple-zone Focusing) based ultrasound imaging techniques using orthogonally frequency-divided chirp signals are investigated. In the simulations, the frequency divided sub-band chirps that have orthogonal property are designed within the frequency bandwidth of the CMUT arrays, and simultaneously fired on multiple ranges, in which each signal is focused at a different range, in one transmission event. This paper also presents ultrasound images through a modulation and demodulation process of orthogonal sub-band coded signals. Experiments on the chirp-coded excitation in CMUT arrays are reported in this paper as a feasibility study of the FDMA (frequency division multiple access) like STMF method. In the experiment, mixed two orthogonal chirp signals are simultaneously fired with the CMUT arrays and the received signals are successfully separated into two compressed signals.

Realistic fetus skin color processing for ultrasound volume rendering

This paper proposes realistic fetus skin color processing using a 2D color map and a tone mapping function (TMF) for ultrasound volume rendering. The contributions of this paper are a 2D color map generated through a gamut model of skin color and a TMF that depends on the lighting position. First, the gamut model of fetus skin color is calculated by color distribution of baby images. The 2D color map is created using a gamut model for tone mapping of ray casting. For the translucent effect, a 2D color map in which lightness is inverted is generated. Second, to enhance the contrast of rendered images, the luminance, color, and tone curve TMF parameters are changed using 2D Gaussian function that depends on the lighting position. The experimental results demonstrate that the proposed method achieves better realistic skin color reproduction than the conventional method.

Speckle reduction using similar block images between adjacent slices for 3D ultrasound images

This paper proposes 3D speckle reduction method using the similarity block image for the speckle reduction in the 3D ultrasound volume image. The proposed method regenerates the 3D volume that increases the 3D inter-frame consistency by generating similar images to the current frame from the adjacent frames. In the multiscale volume transformed by a 3D wavelet, the speckle is reduced and the edge of the tissue is enhanced via anisotropic diffusion. The 3D contrast enhancement algorithm using the coefficient adjustment function in the multiscale volume domain is also proposed. The experimental results demonstrate that the proposed method achieves better performance in terms of all of the PSNR and CNR than the conventional methods.