Influence of missing array elements on phase aberration correction for medical ultrasound

The emerging standard of High Efficiency Video Coding (HEVC) provides a 50% improved compression performance compared to its predecessor H.264/AVC at the expense of increasing its computational complexity which is due to a large number of modes involved in the decision process where it would increase encoding time and provide hardware limitations. In this paper, the reduction of intra-coding complexity is proposed by utilization of Rate-Distortion Optimization (RDO) in preselecting a mode with the best RD cost, among all 35 intra-modes. Two cost functions – Sum of Absolute Difference (SAD) and Sum of Absolute Transform Difference (SATD, available in the HEVC Test Model (HM) Reference Software, are determined by making encoder decisions. This paper compares the performance and complexity cost of SAD only for intra-mode, with SATD-based intra-mode by modifying the HM reference encoder through forced selection of the mentioned modes. Results show that SAD consistently produces the least encoding time among all modes, along with sensible values of bitrate. Furthermore, error concealment was introduced in decoding the video sequences with the use of IEEE 802.22 Standard which only allowed power gains resulting to packet error rates (PER) ranging from 1% to 30% for the analysis on the behavior of each video sequence in an environment where error is introduced.

The sum of absolute difference (SAD) is generally adopted as a cost function in motion estimation (ME) and temporal error concealment (TEC) algorithm owing to its efficiency. The hardware architecture of SAD also consumes considerable power dissipation in video codec chip. Hence, the switching-activity analysis on SAD is quite essential from algorithm/architecture perspectives. This work develops the estimation formulas for switching-activity dedicated for SAD engine according to probability theory. The experiment results reveal that the probability error rate (PER) of the SAD engine is as minor as 5.61%. Consequently, this leads to a precise switching-activity estimation of the SAD-based algorithm/architecture for video signal processing.

This paper deals with problems of surface object recognition in urban environmental conditions in infrared sequences using the template matching technique. Target recognition is always necessary in the selection and tracking the defined objects. The objects feature is changed in dependence on time and space condition such as weather condition, urban environment, etc. In this article, we proposed a method for targets recognition using template matching technique in infrared (IR) video sequences. A user simply chooses the given object at some point during detection. On the basis of feature of selected object, the algorithm employed the template matching techniques to find the object. The designed algorithm was tested in program MATLAB and MATLAB - SIMULINK. The paper will deal with a new algorithm using template matching. The idea of the paper will approach to reduce processing time and increase precision for object detection and selection in using template matching technique. There are many similarities criteria for template matching that are sum of absolute differences (SAD), locally scaled sum of absolute difference (LSAD), zero - mean sum of absolute differences (ZSAD), sum of squared differences (SSD), etc. In this paper we have employed the similarity criteria normalized cross correlation. The cross correlation is not invariant to changes in image intensity such as lighting conditions, and the range of correlation coefficient is dependent on the size of the feature, while we can normalize for the effect of changing intensity and template size by using normalized cross correlation. The basic principle of the algorithm is based on the assumption the object is selected at time t with the center of mass T and we also know the greatest relative velocity between the camera and the target ν ct . Thus, the greatest distance which the target can move after the interval Δt will be limited in the circle with the center T at time t and radius R. That means the size of compared image will be smaller than the size of original frame in infrared video sequence. Therefore, the computational time will be faster. In order to reduce the unnecessary computational time of the template matching technique in whole frame, we need to keep the requirement of the selected maximum size of the target is smaller than a half of the size of the frame. It means that the maximum size of the image ƒ(x, y) is equal to the size of the frame in video sequence.

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

Time delay estimation is a very important operation in ultrasound time-domain flow mapping and correction of phase aberration of an array transducer. As the interest increases in the application of one and a half-dimensional (1.5-D) and two-dimensional (2-D) array transducers to improving image quality and three-dimensional (3-D) imaging, the need of simple, fast, and sufficiently accurate algorithms for real-time time delay estimation becomes exceedingly crucial. In this paper, we present an adaptive time-delay estimation algorithm which minimizes the problem of noise sensitivity associated with the one bit correlation while retaining simplicity in implementation. This algorithm converts each sample datum into a two bit representation including the sign of the sample and an adaptively selected threshold. A bit pattern correlation operation is applied to find the time delay between two engaged signals. By using the criterion of misregistration as an indicator, we are able to show that the proposed algorithm is better than one bit correlation in susceptibility to noise level. Analytical results show that the improvement in reducing misregistration of the two bit correlation over its counterpart is consistent over a wide range of noise level. This is achieved by an adaptive adjustment of the threshold to accommodate signal corruption due to noise. The analytical results are corroborated by results from simulating the blood as a random distribution of red blood cells. Finally, we also present a memory-based architecture to implement the two bit correlation algorithm whose computation time does not depend upon the time delay of the signals to be correlated.

For an efficient analysis the estimation of the noise level in images is very important to specific estimates of each modality. Moreover, it is a fundamental step and indispensable procedure for a number of image processing approaches. Especially in magnetic resonance images (MRI) due to the Rician presented in these, where the level of noise must be evaluated. In this paper a new method to estimate the noise level in MR images T1-w is proposed and compared with a known level of the ration of signal and noise presented. The advantage of this is its easiness for utilization during image acquisition and of course the adaptability of the idea of other areas of body. The correctness of the evaluation is addressed by comparison of Atlas noise free images where the level of Rician noise was artificially added and known. The main idea is the matching of same slices after registration in order to evaluate the level of noise. For evaluation of the range of noise in an image we used the signal noise ratio - SNR and a set of MRI with increasing levels of Rician noise. However, others metrics like the normalized cross correlation - NCC or the Root Mean Squared Error (RMSE) could be used as well.

Using visual odometry and inertial measurements, indoor and outdoor positioning systems can perform an accurate self-localization in unknown, unstructured environments where absolute positioning systems (e.g. GNSS) are unavailable. However, the achievable accuracy is highly affected by the residuals of calibration, the quality of the noise model, etc. Only if these unavoidable uncertainties of sensors and data processing can be taken into account and be handled via error propagation, which allows to propagate them through the entire system. The central filter (e.g. Kalman filter) of the system can then make use of the enhanced statistical model and use the propagated errors to calculate the optimal result. In this paper, we focus on the uncertaintiy calculation of the elementary part of the optical navigation, the template feature matcher. First of all, we propose a method to model the image noise. Then we use Taylor’s theorem to extend two very popular and efficient template feature matchers sum-of-absolute-differences (SAD) and normalized-cross-correlation (NCC) to get sub-pixel matching results. Based on the proposed noise model and the extended matcher, we propagate the image noise to the uncertainties of sub-pixel matching results. Although the SAD and NCC are used, the image noise model can be easily combined with other feature matchers. We evaluate our method by an Integrated Positioning System (IPS) which is developed by German Aerospace Center. The experimental results show that our method can improve the quality of the measured trajectory. Moreover, it increases the robustness of the system.

Purpose: To determine the robustness and accuracy of an open source 2D/3D GPU accelerated image registration algorithm in the context of cranial image guided radiotherapy. Methods: The open source 2D/3D image registration algorithm, Reg23, has been released under the GNU license. The algorithm utilizes an iterative digitally reconstructed radiograph (DRR) approach to the image registration problem. The DRR generator is accelerated on a GPU to rapidly iterate the optimization process. Multiple cost functions are supported and were analyzed. Robustness was determined by comparing a baseline set of orthogonal kV images of a cranial phantom with a predetermined isocenter to the planned isocenter in the CT image set and introducing more than 6000 combinations of rotations and translation to the position of the isocenter in the CT. Accuracy and time efficiency of various cost function were analyzed for the virtual patient shifts. Furthermore, a set of 43 experimental orthogonal images were acquired with a linac mounted kV imaging system of predetermined physical shifts which were compared to the results of the Reg23 algorithm. Results: The Reg23 algorithm was found to be accurate to 0.04±0.02mm for the virtual isocenter shifts and 0.23±0.40mm for real images compared to the CBCT registration results. Time to solution could be reduced from >70 s to < 40 s without a significant change in the algorithm accuracy depending upon the cost function employed. Conclusions: The Reg23 algorithm is robust and sensitive to sub‐mm variations of virtual shifts of the isocenter position. The Normalized Cross Correlation (NCC) cost function was determined to be most accurate and fastest for cranial image registration. For real experimental data, the Gradent Difference (GD) cost function was most accurate and both GD and NCC delivered results accurate to within 0.5 mm and 0.4° when compared to CBCT/CT registrations.

Sub-pixel image registration on an embedded Nanosatellite Platform

In this paper a new early detection algorithm for all-zero coefficient blocks before integer transformation and quantization is proposed in very low bit rate video coding for H.264/AVC standard. The all-zero detecting threshold is very critical as a tradeoff between encoder complexity and image quality. So firstly a feasible threshold range is analyzed theoretically, then based on extensive experiments on typical video sequences from the statistical opinion, a better optimal threshold is discussed, which has a better balance than the theoretically optimal threshold between mis-justice and leak-out ratio in detecting all-zero coefficient blocks. In our proposed algorithm, the sum of absolute difference (SAD) of each motion prediction error in the inter block is used, and this SAD is a bypass obtained from motion estimation process, so no additional computation is required. The simulation results show that the proposed algorithm can reduce the computational load significantly, almost without video quality loss.

Three-dimensional reconstruction of asphalt road surface texture based on binocular stereo vision

Reversible image watermarking schemes are used to protect ownership and copyrights of digital images. This paper proposes a novel reversible image watermarking scheme based on adaptive image interpolation, segmentation and additive prediction error expansion (PEE). Proposed interpolation comprises of weighted average of neighboring pixels by allocating higher and lower weights to less and more distant neighboring pixel values respectively. The proposed adaptive image interpolation focuses on detection of edges thus minimizing artifacts imposed by interpolation. The idea of embedding varying amount of watermark bits in different image segments has been explored. Simple linear iterative clustering (SLIC) based image segmentation is performed to separate very sharp, sharp, smooth and very smooth regions in image. Higher number of watermark bits are embedded in sharp regions by using additive prediction error expansion embedding technique. Simulations of proposed and existing techniques were performed on different images and compared using embedding capacity (EC), peak signal to noise ratio (PSNR), computational efficiency, image quality, mean square error (MSE), normalized cross correlation (NCC) and structural similarity index (SSIM). The experimental results show that proposed scheme achieves better results in terms of EC, PSNR, computational efficiency, image quality, MSE, NCC and SSIM as compared to existing techniques.

<strong>Purpose:</strong> Accurate assessment of the size and location of osteolytic regions is essential in minimally invasive hip revision surgery. Moreover, image-guided robotic intervention for osteolysis treatment requires precise localization of implant components. However, high density metallic implants in proximity to the hip make assessment by either 2D or 3D x-ray imaging difficult. This paper details the initial implementation and evaluation of an advanced model-based conebeam CT (CBCT) reconstruction algorithm to improve guidance and assessment of hip osteolysis treatment. <strong>Method:</strong> A model-based reconstruction approach called Known Component Reconstruction (KCR) was employed to obtain high-quality reconstruction of regions neighboring metallic implants. KCR incorporates knowledge about the implant shape and material to precisely reconstruct surrounding anatomy while simultaneously estimating implant position. A simulation study involving a phantom generated from a CBCT scan of a cadaveric hip was performed. Registration accuracy in KCR iterations was evaluated as translational and rotational error from the true registration. Improvement in image quality was evaluated using normalized cross correlation (NCC) in two regions of interest (ROIs) about the femoral and acetabular components. <strong>Result:</strong> The study showed significant improvement in image quality over conventional filtered backprojection (FBP) and penalized-likelihood (PL) reconstruction. The NCC in the two ROIs improved from 0.74 and 0.81 (FBP) to 0.98 and 0.86 (PL) and &gt;0.99 for KCR. The registration error was 0.01 mm in translation (0.02&deg; in rotation) for the acetabular component and 0.01 mm (0.01&deg; rotation) for the femoral component. <strong>Conclusions:</strong> Application of KCR to imaging hip osteolysis in the presence of the implant offers a promising step toward quantitative assessment in minimally invasive image-guided osteolysis treatment. The method improves image quality (metal artifact reduction), yields a precise registration estimate of the implant, and offers a means for reducing radiation dose in intraoperative CBCT.

Estimation of delay time between precipitation and groundwater level in the middle mountain area of Pyoseon watershed in Jeju Island using moving average method and cross correlation coefficient

Autofocus methods in SAR and self-survey techniques in SONAR have a common mathematical basis in that they both involve estimation and correction of phase errors introduced by sensor position uncertainties. Time delay estimation and correlation methods have been shown to be effective in solving the self-survey problem for towed SONAR arrays. Since it can be shown that platform motion errors introduce similar time-delay estimation problems in SAR imaging, the question arises as to whether such techniques could be effectively employed for autofocus of SAR imagery. With a simple mathematical model for motion errors in SAR, we will show why such correlation/time-delay techniques are not nearly as effective as established SAR autofocus algorithms such as phase gradient autofocus or sub-aperture based methods. This analysis forms an important bridge between signal processing methodologies for SAR and SONAR.