MODELING OF 3-D IMAGES AND SPACE MARKOV CUBIC MESH MODELS

Three-dimensional(3-D)Markov cubic random mesh models are presented andproved in the form of two theorems in details.Its applications to the modeling and description of3-D images are described.The model presented here is a appropriate mathematical tool for thesegmentation,modeling,classification and other processing.Finally,an example is given.

An Experimental System Development for Head Posture Estimation Based on 3-D Images Measurement

Although automobile is an indispensable vehicle to modern life, it also serves as a social problem with a big traffic accident. Among the reasons of traffic accidents, careless driving accounts for the largest part. So in order to avoid the careless driving, a system which can measure the posture of a driver and warns driver to drive carefully in the case of looking aside is necessary. Although the image measurement method is used broadly, there is a problem on which measurement accuracy is influenced by environment light, makeup of the driver, etc. in the general method based on the two-dimensional image. Therefore, in this study, we propose an image measurement method to obtain the head posture of driver. First we use three-dimensional measurement method which based on the infrared pattern projection to get 3-D information of head, and then we calculate the angle for faces. In this paper, we explain the composition method of an experiment system, and the results of head posture measurement experiment.

An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.

(1-3)-β-D葡聚糖联合降钙素原、CD4^(+)T淋巴细胞多指标在艾滋病患者马尔尼菲篮状菌感染早期诊断临床研究

目的探讨(1-3)-β-D葡聚糖联合降钙素原(procalcitonin,PCT)、CD4^(+)T淋巴细胞多指标在艾滋病患者马尔尼菲篮状菌感染早期诊断临床研究。方法回顾性选取我院2020年1月—2022年6月住院的120例艾滋病患者为研究对象。依据实验室结果,将其分为马尔尼菲篮状菌感染确诊组(血或组织液培育养出马尔尼菲篮状菌),简称A组(62例),及马尔尼菲篮状菌感染临床诊断组[根据临床症状、体征、血常规及(1-3)-β-D葡聚糖、PCT、CD4^(+)T淋巴细胞多指标诊断],简称B组(58例)。检测患者(1-3)-β-D葡聚糖、PCT、CD4^(+)T淋巴细胞的表达水平,采用受试者工作特征(receiver-operating characteristic,ROC)曲线下面积(area under the curve,AUC)评估上述指标联合检测对艾滋病患者感染马尔尼菲篮状菌的诊断效能。结果A组的(1-3)-β-D葡聚糖和PCT水平均高于B组,CD4^(+)T淋巴细胞个数低于B组(P<0.05);(1-3)-β-D葡聚糖、PCT、CD4^(+)T淋巴细胞联合检测的AUC为0.933,(1-3)-β-D葡聚糖单独检测的AUC是0.812,PCT单独检测的AUC为0.883,CD4^(+)T淋巴细胞单独检测的AUC是0.810,(1-3)-β-D葡聚糖、PCT和CD4^(+)T淋巴细胞联合检测的AUC皆优于三项单独检测,表明(1-3)-β-D葡聚糖、PCT和CD4^(+)T淋巴细胞联合检测的诊断价值皆优于单一指标诊断,且联合检测的特异度、约登指数分别为92.43%和0.580,均高于三项单独检测。结论(1-3)-β-D葡聚糖联合PCT和CD4^(+)T淋巴细胞多指标对艾滋病马尔尼菲篮状菌感染具有非常高的临床诊断价值,能够帮助医生分析出高危风险患者,及时制定治疗方案,同时也承担预后效果的判断依据,对治疗艾滋病马尔尼菲篮状菌感染具有非常重要的研究价值。

1例RHD-CE(3-7)-D基因重组与RHCE变异型患者的血清学与分子生物学分析

目的 研究分析1例Rh血型弱D、弱cE患者的血清学与分子生物学特征,为该类患者的临床安全输血提供实验依据。方法 采用微柱凝胶卡法对患者红细胞进行ABO、RhDCcEe抗原的鉴定,同时采用试管法进行血型复核,抗人球蛋白卡法筛查不规则抗体;采用PCR-SSP法对RhDCcEe(RhD、RhC、Rhc、RhE、Rhe)基因型进行检测;三代全长测序技术对RHD/RHCE基因序列进行测序分析。结果 微柱凝胶卡法鉴定ABO、RhD、RhCcEe血型抗原的结果为:A抗原(-)、B抗原(-)、RhD(1+)、RhC(4+)、Rhc(1+)、RhE(1+)、Rhe(4+)、对照孔(-);试管法ABO、RhD、RhCcEe抗原鉴定该患者表型为:A抗原(-)、B抗原(-)、RhD(w+)、RhC(4+)、Rhc(w+)、RhE(w+)、Rhe(4+),对照管(-);抗人球蛋白卡法筛查患者不规则抗体阴性;PCR-SSP法血型基因分型RhDCcEe结果:RhD(+)、RhC(+)、Rhc(+)、RhE(+)、Rhe(+);RHD/RHCE基因结果:RHD单倍体1为外显子1-10全缺失,而单倍体2为外显子RHD-CE基因重组融合,且确认其重组类型为RHD-CE(3-7)-D,起点在外显子2(g.20238-20312之间),终点在外显子8(g49184-50480之间),同时RHCE基因第6外显子存在新碱基点突变RHCE*cE(827C>A)。结论RHD-CE(3-7)-D基因重组融合与RHCE*cE(827C>A)新等位基因突变可能引起D、cE血型抗原弱表达,为临床安全输血提供了重要的实验数据支持。

Efficient single-pixel imaging encrypted transmission based on 3D Arnold transformation

Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.

MACS-W:A modified optical clearing agent for imaging 3D cell cultures

Three-dimensional(3D)cell cultures have contributed to a variety of biological research fields by filling the gap between monolayers and animal models.The modern optical sectioning microscopic methods make it possible to probe the complexity of 3D cell cultures but are limited by the inherent opaqueness.While tissue optical clearing methods have emerged as powerful tools for investigating whole-mount tissues in 3D,they often have limitations,such as being too harsh for fragile 3D cell cultures,requiring complex handling protocols,or inducing tissue deformation with shrinkage or expansion.To address this issue,we proposed a modified optical clearing method for 3D cell cultures,called MACS-W,which is simple,highly efficient,and morphology-preserving.In our evaluation of MACS-W,we found that it exhibits excellent clearing capability in just 10 min,with minimal deformation,and helps drug evaluation on tumor spheroids.In summary,MACS-W is a fast,minimally-deformative and fluorescence compatible clearing method that has the potential to be widely used in the studies of 3D cell cultures.

Parallel computing approach for efficient 3-D X-ray-simulated image reconstruction

Accurate 3-dimensional(3-D)reconstruction technology for nondestructive testing based on digital radiography(DR)is of great importance for alleviating the drawbacks of the existing computed tomography(CT)-based method.The commonly used Monte Carlo simulation method ensures well-performing imaging results for DR.However,for 3-D reconstruction,it is limited by its high time consumption.To solve this problem,this study proposes a parallel computing method to accelerate Monte Carlo simulation for projection images with a parallel interface and a specific DR application.The images are utilized for 3-D reconstruction of the test model.We verify the accuracy of parallel computing for DR and evaluate the performance of two parallel computing modes-multithreaded applications(G4-MT)and message-passing interfaces(G4-MPI)-by assessing parallel speedup and efficiency.This study explores the scalability of the hybrid G4-MPI and G4-MT modes.The results show that the two parallel computing modes can significantly reduce the Monte Carlo simulation time because the parallel speedup increment of Monte Carlo simulations can be considered linear growth,and the parallel efficiency is maintained at a high level.The hybrid mode has strong scalability,as the overall run time of the 180 simulations using 320 threads is 15.35 h with 10 billion particles emitted,and the parallel speedup can be up to 151.36.The 3-D reconstruction of the model is achieved based on the filtered back projection(FBP)algorithm using 180 projection images obtained with the hybrid G4-MPI and G4-MT.The quality of the reconstructed sliced images is satisfactory because the images can reflect the internal structure of the test model.This method is applied to a complex model,and the quality of the reconstructed images is evaluated.

The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective:To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system(BI-RADS)category 3 and above.Methods:From June 2021 to July 2022,163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects.After pathological diagnosis,24 cases were malignant breast nodules of BI-RADS 3 or above,while 139 cases were benign breast nodules of BI-RADS 3 or above.The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above,including 95%CI,was observed and analyzed.Results:The malignant and benign detection rates of conventional ultrasound were 88.63%and 75.00%,respectively,and the malignant and benign detection rates of ultrasound imaging were 93.18%and 87.50%,respectively,with 95%CIs greater than 0.7.Conclusion:Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

血浆(1-3)-β-D葡聚糖诊断新生儿侵袭性真菌感染的价值

探讨在针对新生儿侵袭性真菌感染的诊断中使用血浆(1-3)-β-D葡聚糖能够产生的诊断效能。方法 选取2021年1月到2022年2月因为侵袭性真菌感染于医院寻求医疗干预的新生儿总共60例作为本次研究的一部分研究对象记为1组;另外选取60例健康婴儿作为2组;统计、对比两组患者的血浆(1-3)-β-D葡聚糖水平、CD4+T淋巴细胞数水平;整理PCR技术针对1组患者诊断结果,将其与血浆(1-3)-β-D葡聚糖诊断侵袭性真菌感染的诊断效能进行对比。结果 研究结果显示1组患者的血浆(1-3)-β-D葡聚糖水平、CD4+T淋巴细胞数水平较之于2组患者存在明显差异,1组患者血浆(1-3)-β-D葡聚糖水平更高,而CD4+T淋巴细胞数水平明显更低;P<0.05。在针对诊断效能的考察上,血浆(1-3)-β-D葡聚糖诊断效能和PCR诊断结果之间在灵敏度、特异度方面差异不大,P>0.05。结论 针对罹患侵袭性真菌感染的新生儿使用血浆(1-3)-β-D葡聚糖进行检查,一定程度上能够确保诊断结果的严谨程度,可使用。

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Image Non-Uniformity Correction in 3T Gd-EOB-DTPA-Enhanced Magnetic Resonance Imaging: Comparison among Different Software Versions

Background: Non-uniformity in signal intensity occurs commonly in magnetic resonance (MR) imaging, which may pose substantial problems when using a 3T scanner. Therefore, image non-uniformity correction is usually applied. Purpose: To compare the correction effects of the phased-array uniformity enhancement (PURE), a calibration-based image non-uniformity correction method, among three different software versions in 3T Gd-EOB-DTPA-enhanced MR imaging. Material and Methods: Hepatobiliary-phase images of a total of 120 patients who underwent Gd-EOB-DTPA-enhanced MR imaging on the same 3T scanner were analyzed retrospectively. Forty patients each were examined using three software versions (DV25, DV25.1, and DV26). The effects of PURE were compared by visual assessment, histogram analysis of liver signal intensity, evaluation of the spatial distribution of correction effects, and evaluation of quantitative indices of liver parenchymal enhancement. Results: The visual assessment indicated the highest uniformity of PURE-corrected images for DV26, followed by DV25 and DV25.1. Histogram analysis of corrected images demonstrated significantly larger variations in liver signal for DV25.1 than for the other two versions. Although PURE caused a relative increase in pixel values for central and lateral regions, such effects were weaker for DV25.1 than for the other two versions. In the evaluation of quantitative indices of liver parenchymal enhancement, the liver-to-muscle ratio (LMR) was significantly higher for the corrected images than for the uncorrected images, but the liver-to-spleen ratio (LSR) showed no significant differences. For corrected images, the LMR was significantly higher for DV25 and DV26 than for DV25.1, but the LSR showed no significant differences among the three versions. Conclusion: There were differences in the effects of PURE among the three software versions in 3T Gd-EOB-DTPA-enhanced MR imaging. Even if the non-uniformity correction method has the same brand name, correction effects may differ depending on the software version, and these differences may affect visual and quantitative evaluations.

A network lightweighting method for difficult segmentation of 3D medical images

Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.

Synthesizing monochromatic 3-D images by multiple-exposure rainbow holography with vertical area-partition approach

We report for the first time the theoretical analysis and experimental results of a white-light reconstructed monochromatic 3-D image synthesizing tomograms by multiple rainbow holo-graphy with vertical-area partition (VAP) approach. The theoretical and experimental results show that 3-D monochromatic image can be synthesized by recording the master hologram by VAP ap-proach without any distortions either in gray scale or in geometrical position. A 3-D monochromatic image synthesized from a series of medical tomograms is presented in this paper for the first time.

Fast Encoding-Decoding of 3D Hyperspectral Images Using a Non-Supervised Multimodal Compression Scheme

We introduce in this paper an extension of the Multimodal Compression technique (MC) for the purpose of coding hyperspectral image sequences. The main idea requires few steps, namely: (1) reducing the size of the sequence by inserting smooth images containing less information into the remaining images of the same sequence, (2) then coding the new compacted sequence using 3D-SPIHT algorithm. In this new scheme, called MC-3D-SPIHT, the insertion is achieved only in the contour of each image, according to a non-supervised way, so that one can preserve the Region of Interest (ROI) quality. For this purpose, a mixing function is employed. After the decoding process, inserted images are extracted by a separation function and the original sequence is reconstructed. By considering data from AVIRIS database, we will show how one decrease significantly the computing time for both coding and decoding.

3-D Visualization of Medical Images with Arbitrary Sections

In this paper, with the general retrospect to the research on surface reconstruction and the marching cubes algorithm, we gave detailed description of an algorithm on the construction of object surfaces. The possible ambiguity problem in the original marching cubes algorithm was eliminated by its index mechanism. Some results on the MRI images were presented. Based on extracting and clipping contours from a set of medial slice images and setting the patch vertices values according to the gray images, this algorithm may be applied to form the arbitrary section images with three dimensional effects. It can also enhance the visual effect and interpretation of medical data.

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

3-Dimensional Irradiance Imaging with a Single Camera System

本文提出了用一个摄像机重建三维发光体辐射场分布的方法．连续的三维发光体可以看成若干个互相平行的二维发光断层的组合，对发光体某断层进行聚焦摄像，得到的图像是该断层的高斯像和其他断层的离焦像的叠加像．对不同断层分别聚焦摄像，得到一组辐射图像，对图像进行变换、处理，采用图像反演算法，可以重建各断层的辐射场．本文用实验装置模拟了具有两断层的三维发光体，获得了初步结果，证明了该理论的正确性．