Baek-Sop Kim

P2B-3 A New Motion Estimation and Compensation Method for Real-Time Ultrasonic Synthetic Aperture Imaging

In synthetic aperture imaging (SAI), it is well known fact that focusing quality will be degraded without estimation and compensation of the target movement. Various methods are available for compensation of the target movement. In this paper, simpler and more robust computational method compare to the conventional method will be presented. Presented method combines autocorrelation method of conventional two dimensional- tissue Doppler imaging (2D-TDI) with SAI, only the transmit sequence is appropriately changed. Computer simulation and the phantom experiment are used to show effectiveness of the presented method.

A computational method for detecting copy number variations using scale-space filtering

BackgroundAs next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.ResultsHere, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the finger print map, the contours of zero-crossing points for various scales.ConclusionsThe performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/.

Detection of copy number variation using scale space filtering

This study proposes a novel CNV detection algorithm based on scale space filtering. It uses Gaussian filter for the convolution with a scale parameter. The range of the scale parameter is adjusted according to the coverage level of read data. The position of a CNV region is determined through a coarse and a fine searches over the scales. The results showed low dependency of the performance of the proposed method on the coverage level compared to the conventional methods. The results also showed that the proposed method outperforms the conventional methods by 63.29 ∼ 73.57 %.

P2B-2 A New Architectural Design of Full Aperture, Full Frame-Rate Synthetic Aperture Beamforming ASIC

In ultrasound imaging, synthetic aperture imaging (SAI) supported by full aperture and full frame rate is desirable for an improved resolution, SNR and real time imaging, however, real time implementation requires RF data from all channels have to be transferred to all scan-lines in real time so that implemented hardware become very complex and this complexity hindered to the practical implementation. In this paper, a new intuitive and simple architecture which minimizes this complexity, especially the interconnections between Rx channel (from ADC) and ASIC, is presented. This architecture contains high speed serial data transfer (employing low voltage differential signaling - LVDS), division of the channels into groups and RF data sharing bus. Also modular structure is employed so that ASIC can be repeatedly placed for easy system implementation. Chip area (CA) and power consumption (PC) have been estimated for the system with proposed architecture and it is been confirmed that ASIC can be constructed using a 0.13 um semiconductor process.

Coherence adaptive speckle filter for ultrasound image

In this paper, we propose a coherence adaptive speckle filter to reduce the speckle in ultrasound images. The conventional adaptive filters, which are used for speckle filtering of SAR images, used SNR measure to discriminate the noise and signal. In case of ultrasound images, the connectivity of the structure elements is important. Therefore, we propose an adaptive speckle filter, which uses the coherence measure to discriminate the signal from noise. The proposed method is compared to the conventional Lee filter using contrast-to-noise ratio (CNR). It has been shown that the proposed method gives higher CNR, and less sensitive to noise.

Robust control of linear systems with nonlinear uncertainties via disturbance observer techniques

This paper considers robust control problems for linear systems with matched nonlinear uncertainties. In this paper, the uncertainties are considered as disturbances and a disturbance observer is designed. By subtracting these estimated values of disturbances from the plant input, the performance degradation due to the uncertainties can be improved. The estimation error is shown to be bounded and made arbitrarily small if the estimator gain is chosen large enough. To get rid of the influences of these errors on the performance, the additional robust min-max control input is added with the disturbance observer output. By the way, the min-max control input needs to be switched and provokes chattering phenomena. The control method suggested in this paper can, however, reduce chattering phenomena as small as possible by choosing large disturbance observer gain because the magnitude of the switching control input is determined by the ones of the disturbance estimation error bounds.

Determination of initial configuration for LVQ by using CNN

A method for determining the initial configuration for the LVQ is proposed. It is based on the condensed nearest neighbor (CNN) rule followed by the K-means clustering method. Experiments show that the proposed method is generally better than the conventional ones which use k-NN or the K-means. And it is also shown that the performance of the CNN is improved by applying the LVQ as a post processing.

Shape-based retrieval of CNV regions in read coverage data

This study proposes a novel copy number variation (CNV) detection method, CNV_shape, based on variations in the shape of the read coverage data which are obtained from millions of short reads aligned to a reference sequence. The proposed method carries out two transforms, mean shift transform and mean slope transform, to extract the shape of a CNV more precisely from real human data, which are vulnerable to experimental and biological noises. The mean shift transform is a procedure for gaining a preliminary estimation of the CNVs by statistically evaluating moving averages of given read coverage data. The mean slope transform extracts candidate CNVs by filtering out non-stationary sub-regions from each of the primary CNVs pre-estimated in the mean shift procedure. Each of the candidate CNVs is merged with neighbours depending on the merging score to be finally identified as a putative CNV, where the merging score is estimated by the ratio of the positions with non-zero values of the mean shift transform to the total length of the region including two neighbouring candidate CNVs and the interval between them. The proposed CNV detection method was validated experimentally with simulated data and real human data. The simulated data with coverage in the range of 1x to 10x were generated for various sampling sizes and p-values. Five individual human genomes were used as real human data. The results show that relatively small CNVs (> 1 kbp) can be detected from low coverage (> 1.7x) data. The results also reveal that, in contrast to conventional methods, performance improvement from 8.18 to 87.90% was achieved in CNV_shape. The outcomes suggest that the proposed method is very effective in reducing noises inherent in real data as well as in detecting CNVs of various sizes and types.

Ultrasound speckle reduction and edge enhancing in Laplacian pyramid

A new method which reduce speckle noise and enhance contrast is proposed for medical ultrasound imaging. We modified the conventional nonlinear coherent diffusion (NCD) and proposed the edge enhancing nonlinear coherent diffusion (EENCD) for this purpose. The EENCD suppresses the speckle noise in a homogenous region, and it performs both directional filtering on the contour direction and edge enhancement on the tangential direction in a structural region. The EENCD is embodied in the Laplacian pyramid. An image is decomposed into a Laplacian pyramid, and then, the EENCD filtering is done during reconstructing the Gaussian layer images, to produce the filtered image. The proposed method was compared with the conventional CED and NCD methods on the real ultrasound images. It has been shown that the proposed method can enhance the edges and the contrast is enhanced more than the conventional methods.

A CNV detection algorithm based on statistical analysis of the aligned reads

Recently it was found that various genetic structural variations such as CNV(copy number variation) exist in the human genome, and these variations are closely related with disease susceptibility, reaction to treatment, and genetic characteristics. In this paper we propose a new CNV detection algorithm using millions of short DNA sequences generated by giga-sequencing technology. Our method maps the DNA sequences onto the reference sequence, and obtains the occurrence frequency of each read in the reference sequence. And then it detects the statistically significant regions which are longer than 1Kbp as the candidate CNV regions by analyzing the distribution of the occurrence frequency. To select a proper read alignment method, several methods are employed in our algorithm, and the performances are compared. To verify the superiority of our approach, we performed extensive experiments. The result of simulation experiments (using a reference sequence, build 35 of NCBI) revealed that our approach successfully finds all the CNV regions that have various shapes and arbitrary length (small, intermediate, or large size).