Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

The amount of 3D data stored and transmitted in the Internet of Medical Things(IoMT)is increasing,making protecting these medical data increasingly prominent.However,there are relatively few researches on 3D data watermarking.Moreover,due to the particularity of medical data,strict data quality should be considered while protecting data security.To solve the problem,in the field of medical volume data,we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform(PCT and 3D-DCT).Each slice of the volume data was transformed by PCT to obtain feature row vector,and then the reshaped three-dimensional feature matrix was transformed by 3D-DCT.Based on the contour information of the volume data and the detail information of the inner slice,the visual feature vector was obtained by applying the per-ceptual hash.In addition,the watermark was encrypted by a multi-sensitive initial value Sine and Piecewise linear chaotic Mapping(SPM)system,and embedded as a zero watermark.The key was stored in a third party.Under the same experimental conditions,when the volume data is rotated by 80 degrees,cut 25%along the Z axis,and the JPEG compression quality is 1%,the Normalized Correlation Coefficient(NC)of the extracted watermark is 0.80,0.89,and 1.00 respectively,which are significantly higher than the comparison algorithm.

3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

With the introduction of spectral-domain optical coherence tomography(SD-OCT),much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT.Thus,there is a critical need for the development of three-dimensional(3D)segmentation methods for processing these data.We present here a novel 3D automatic segmentation method for retinal OCT volume data.Brie°y,to segment a boundary surface,two OCT volume datasets are obtained by using a 3D smoothingfilter and a 3D differentialfilter.Their linear combination is then calculated to generate new volume data with an enhanced boundary surface,where pixel intensity,boundary position information,and intensity changes on both sides of the boundary surface are used simultaneously.Next,preliminary discrete boundary points are detected from the A-Scans of the volume data.Finally,surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points.Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data.We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system,where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496
512(each B-Scan comprising 512 A-Scans containing 496 pixels);experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

A Light Weight Traffic Volume Prediction Approach Based on Finite Traffic Volume Data

As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches were proposed for the traffic volume prediction from different perspectives. However, most of these approaches are based on a large amount of historical data. When there are only finite collected traffic data, they cannot be well trained, so the prediction accuracy of these approaches will be poor. In this paper, a tensor model is proposed to capture the change patterns of continuous traffic volumes. From collected traffic volume data, the element data are extracted to update the corresponding elements of the tensor model. Then, a tucker decomposition and gradient descent based algorithm is employed to impute the missing elements of the tensor model. After missing element imputation, the tensor model can be directly applied to the short-term traffic volume prediction through searching the corresponding elements of the model and the storage cost of the model is low. Our model is evaluated on real traffic volume data from PeMS dataset, which indicates that our model has higher traffic volume prediction accuracy than other approaches in the situation of finite traffic volume data.

Holographic visualization of volume data based on adjustable ray to optical-wave conversion

A holographic visualization of volume data based on adjustable ray to optical-wave conversion is presented.Computergenerated holograms are generated by emitting multiple rays to sample the volumetric field.Equal interval sampling,object light wave adjustment,and information composition are sequentially performed during the march of rays.The program is accelerated in parallel to reduce the total time,and the reconstructions are dynamically adjusted to express different parts of an object.Optical experiments verify that the proposed method can holographically reconstruct the surface and interior information of objects.

Modeling and volume rendering approach of underwater three-dimensional acoustic energy field

The underwater acoustics is primary and most effective method for underwater object detection and the complex underwater acoustics battlefield environment can be visually described by the three-dimensional(3D)energy field.Through solving the 3D propagation models,the traditional underwater acoustics volume data can be obtained,but it is large amount of calculation.In this paper,a novel modeling approach,which transforms two-dimensional(2D)wave equation into 2D space and optimizes energy loss propagation model,is proposed.In this way,the information for the obtained volume data will not be lost too much.At the same time,it can meet the requirements of data processing for the real-time visualization.In the process of volume rendering,3D texture mapping methods is used.The experimental results are evaluated on data size and frame rate,showing that our approach outperforms other approaches and the approach can achieve better results in real time and visual effects.