Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric cancer

BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

改进Camshift算法实时目标跟踪实现

为解决Camshift目标跟踪算法在跟踪目标遮挡时陷入局部最大值、跟踪目标快速移动导致跟踪丢失以及光照变化影响跟踪精度一系列问题,提出一种改进Camshift目标跟踪算法。利用自适应权重与H通道特征提取模板,融合Kalman滤波算法并引入巴氏距离遮挡判别法。非遮挡时,使用Kalman预测调整跟踪搜索区域;遮挡时,使用Kalman预测跟踪。实验结果表明,将改进后算法部署于FPGA硬件平台能够准确地跟踪快速运动、遮挡干扰目标,在1920×1080分辨率下理论跟踪帧率为98.17帧/s,对1080p@60 Hz以及多种分辨率视频输入下平均跟踪重叠率达到84.68%。

A fracture enhancement method based on the histogram equalization of eigenstructure-based coherence

Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones are difficult to detect. Furthermore, middleand small-scale fractures in fractured zones where migration image energies are usually not concentrated perfectly are also hard to detect because of the fuzzy, clouded shadows owing to low grayscale values. A new fracture enhancement method combined with histogram equalization is proposed to solve these problems. With this method, the contrast between discontinuities and background in coherence images is increased, linear structures are highlighted by stepwise adjustment of the threshold of the coherence image, and fractures are detected at different scales. Application of the method shows that it can also improve fracture cognition and accuracy.

Coherence cube enhancement based on local histogram specification

Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished on coherence sections （time slices or profiles） due to interferences from adjacent strata, especially these with strong reflectivity. In this paper, we propose a coherence enhancement method which applies local histogram specification （LHS） techniques to enhance subtle faults or fractures in the coherence cubes. Unlike the traditional histogram specification （HS） algorithm, our method processes 3D coherence data without discretization. This method partitions a coherence cube into many sub-blocks and self-adaptively specifies the target distribution in each block based on the whole distribution of the coherence cube. Furthermore, the neighboring blocks are partially overlapped to reduce the edge effect. Applications to real datasets show that the new method enhances the details of subtle faults and fractures noticeably.

An improved mean shift tracking algorithm based on double weighted color histogram

In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weaken weight is proposed in the paper firstly.Combining with the object center weight based on the kernel function,the problem of interference of the similar color background can be solved.And then,a model updating strategy is presented to improve the tracking robustness on the influence of occlusion,illumination,deformation and so on.With the test on the sequence of Tiger,the proposed approach provides better performance than the original mean shift tracking algorithm.

GIS局部放电光信号检测技术及光学图像诊断方法

光测法是一种有效的局部放电检测技术。为研究典型局部放电的光学特征和光学图像诊断方法,首先搭建气体绝缘局部放电光电联合检测实验平台,实现光信号和电信号的同步采集;然后分别采用导光柱法和荧光光纤法对SF_(6)气体中典型放电光信号进行实验测量和统计分析,对比研究两种光学检测方法的放电特征参数,包括起始放电电压、相位分布、脉冲幅值和数量等;最后,利用导光柱采集的典型局部放电信号,提出一种基于金字塔梯度直方图(PHOG)特征的局部放电光学图像故障诊断方法。结果显示该模型诊断准确率达90.4%,验证了基于导光柱的局部放电光学检测与诊断的可靠性。

DCE-MRI直方图定量参数联合DWI在乳腺癌早期诊断中的应用价值

目的探讨MRI动态增强扫描(DCE-MRI)直方图定量参数联合扩散加权成像(DWI)在乳腺癌早期诊断中的应用价值。方法回顾性收集2017年9月至2021年9月期间在本院进行手术治疗的120例女性乳腺病变患者,依据术后病理结果分为良性组(n=78)与恶性组(n=42),两组均在手术前进行DCEMRI及DWI检查,依据软件得出DCE-MRI直方图定量参数及ADC值,比较DCE-MRI直方图定量参数及ADC的组间差异,绘制ROC曲线,分析直方图定量参数与DWI联合用于早期诊断乳腺癌的价值。结果相较于良性组,恶性组的K^(trans)中位数、平均值、第90%位数,K_(ep)第90%位数、V_(p)中位数、平均值、第50%位数均高,ADC值低于良性组,差异有统计学意义(P<0.05);绘制ROC曲线图,结果显示,DCEMRI直方图定量参数联合DWI对乳腺癌早期诊断的AUC=0.969>0.7,诊断效能较好,最佳敏感度、特异度为90.50%、96.20%。结论DCE-MRI直方图定量参数联合DWI在乳腺癌早期诊断中具有较高的临床应用价值。

MRI Histogram Texture Feature Analysis of the Optic Nerve in the Patients with Optic Neuritis

Objective To evaluate the optic nerve impairment using MRI histogram texture analysis in the patients with optic neuritis.Methods The study included 60 patients with optic neuritis and 20 normal controls. The coronal T2 weighted imaging(T2 WI) with fat saturation and enhanced T1 weighted imaging(T1 WI) were performed to evaluate the optic nerve. MRI histogram texture features of the involved optic nerve were measured on the corresponding coronal T2 WI images. The normal optic nerve(NON) was measured in the posterior 1/3 parts of the optic nerve. Kruskal-Wallis one-way ANOVA was used to compare the difference of texture features and receiver operating characteristic(ROC) curve were performed to evaluate the diagnostic value of texture features for the optic nerve impairment among the affected optic nerve with enhancement(ONwEN), affected optic nerve without enhancement(ONwoEN), contralateral normal appearing optic nerve(NAON) and NON. Results The histogram texture Energy and Entropy presented significant differences for ONwEN vs. ONwoEN(both P = 0.000), ONwEN vs. NON(both P = 0.000) and NAON vs. NON(both P < 0.05). ROC analysis demonstrated that the area under the curve(AUC) of histogram texture Energy were 0.758, 0.795 and 0.701 for ONwEN vs. ONwoEN, ONwEN vs. NON and NAON vs. NON, AUC of Entropy were 0.758, 0.795 and 0.707 for ONwEN vs. ONwoEN, ONwEN vs. NON and NAON vs. NON.Conclusion The altered MRI histogram texture Energy and Entropy could be considered as a surrogate for MRI enhancement to evaluate the involved optic nerve and normal-appearing optic nerve in optic neuritis.

合成MRI定量直方图特征对预测乳腺癌腋窝淋巴结转移状态的价值

目的:探讨合成MRI基于ALN本身定量直方图特征在预测乳腺癌腋窝淋巴结转移(axillary lymph node metastasis,ALNM)中的潜在价值,并将其预测效能与常规影像征象以及ADC值定量直方图特征进行比较。方法:共纳入104枚乳腺癌腋窝淋巴结(ALNM组45枚,非ALNM组59枚),所有患者接受完整乳腺MR检查,包括合成MRI、DWI和DCE MRI。采用PyRadiomics软件提取合成MRI、ADC的定量直方图特征。采用卡方检验、独立样本t检验及Mann-Whitney U检验比较ALNM组与非ALNM组的合成MRI[T1、T2和质子密度(PD)]、ADC值的直方图特征及其DCE-MRI上的影像征象差异。将差异有统计学意义的参数纳入多因素logistic回归分析,采用ROC的曲线下面积(AUC)评估上述变量预测乳腺癌腋窝淋巴结转移状态的诊断性能。结果:合成MRI中T2-最小值、ADC-10th及淋巴结DCE-MRI影像征象(短径)为鉴别乳腺癌腋窝淋巴结转移状态的独立预测因素(P值分别为0.041、0.022、0.023),而T2-最小值所建立的预测模型具有较好的诊断效能[0.765(95%CI:0.673~0.858)]并优于单独ADC(ADC-10th)、影像征象(短径)预测模型,此外,合成MRI(T2-最小值)+ADC(ADC-10th)+影像征象(短径)所建立的多参数模型可显著提高对乳腺癌腋窝淋巴结转移状态的整体诊断效能。结论:合成MRI基于ALN本身定量参数直方图特征在预测乳腺癌腋窝淋巴结转移状态中具有一定价值,合成MRI+ADC定量参数直方图特征+淋巴结影像征象多参数模型预测效能最优,这种多参数联合模型可能是一种预测ALNM有前景的替代方法。

基于HE-CSR的红外与可见光图像改进融合方法

红外与可见光图像由于二者之间存在互补特性而成为图像融合研究领域重要的源图像。目前红外与可见光图像融合方法存在的一个问题是图像中纹理信息的细节保存能力有限。为解决此问题,首先采用基于直方图均衡化(HE)的方法分别对配准后红外和可见光图像灰度值的范围进行动态扩展,实现图像增强,使得图像中的纹理信息更加突出,同时图像的背景与纹理细节之间的对比度也得以提高。其次,采用L0梯度最小化滤波器分别对增强后的图像进行平滑处理,得到图像的背景层,然后通过源图像与背景层进行差运算得到细节层,实现红外和可见光图像的分解。再次,将卷积稀疏表示(CSR)与特征相似性分析结合应用于红外与可见光图像融合:对两个包含丰富纹理信息的细节层采用基于卷积稀疏表示的融合策略进行融合,该过程中为了降低卷积稀疏表示的误配敏感度,采用基于窗口的平均策略对图像活动水平图进行处理,使卷积稀疏表示对误配不敏感;针对背景图像中存在大量冗余信息的问题,对两个背景层进行特征相似性分析,并以此作为确定二者在的融合过程中的重要程度的依据。最后,通过L0梯度最小化图像分解的逆变换重构初步融合后的细节层和背景层,得到红外与可见光图像的融合结果。采用TNO数据集中21个场景的场景1(建筑)和场景2(树林)两组图像进行主观视觉分析,观测结果表明基于HE-CSR的融合方法较现有CVT、DTCWT、FPDE、GTF、IFEVIP、LP、RP和CSR共8种图像融合典型方法,在视觉上更好地保留了图像中的纹理细节;同时,进一步对TNO数据集所有场景图像融合效果进行客观指标评价,结果显示基于HE-CSR的融合结果的SF、SD、SCD、AG、EN、CC的6个评价指标值分别为7.3166、37.3505、1.7041、5.5714、6.7563和0.7446,分别提高了19.54%、21.87%、13.11%、31.31%、2.17%和8.23%。实验结果表明,所提出的HE-CSR融合方法在主观视觉分析和客观指标评价上都优于其他典型方法,为红外与可见光图像融合提供一种新的更有效的模型及方法。

定量磁化率成像在评估直肠癌病理T分期、脉管和神经侵犯中的初步应用

目的探讨定量磁化率成像(QSM)技术在评估直肠癌病理T分期(pT)、脉管和神经侵犯中的应用价值。方法回顾性分析经病理证实并于术前行直肠QSM检查的29例直肠癌病人资料,根据病理结果中的pT分期将病人分为pT1-2期组(13例)和pT3-4期组(16例),根据有无脉管侵犯分为脉管侵犯阳性组(9例)和阴性组(20例),根据有无神经侵犯分为神经侵犯阳性组(3例)和阴性组(26例)。在MATLAB version 2018b平台上对QSM影像进行处理,将得到的QSM加权影像导入FireVoxel build 394D软件(https://firevoxel.org/)进行分割,并由软件自动生成肿瘤组织的磁化率直方图参数。采用Mann-Whitney U检验比较各组间的组织磁化率值。结果pT3-4期第60、70、80、90百分位的组织磁化率值高于pT1-2期组(均P<0.05)。脉管侵犯阳性组第60、70、80、90百分位的组织磁化率值高于脉管侵犯阴性组(均P<0.05)。神经侵犯阳性组、阴性组的组织磁化率值差异均无统计学意义(均P>0.05)。结论QSM技术可以应用于直肠癌研究,基于QSM技术的定量磁化率值可以反映直肠癌pT分期、脉管和神经侵犯的差异。

Apparent diffusion coefficient-based histogram analysis differentiates histological subtypes of periampullary adenocarcinoma

BACKGROUND For periampullary adenocarcinoma,the histological subtype is a better prognostic predictor than the site of tumor origin.Intestinal-type periampullary adenocarcinoma(IPAC)is reported to have a better prognosis than the pancreatobiliary-type periampullary adenocarcinoma(PPAC).However,the classification of histological subtypes is difficult to determine before surgery.Apparent diffusion coefficient(ADC)histogram analysis is a noninvasive,nonenhanced method with high reproducibility that could help differentiate the two subtypes.AIM To investigate whether volumetric ADC histogram analysis is helpful for distinguishing IPAC from PPAC.METHODS Between January 2015 and October 2018,476 consecutive patients who were suspected of having a periampullary tumor and underwent magnetic resonance imaging(MRI)were reviewed in this retrospective study.Only patients who underwent MRI at 3.0 T with different diffusion-weighted images(b-values=800 and 1000 s/mm^2)and who were confirmed with a periampullary adenocarcinoma were further analyzed.Then,the mean,5th,10th,25th,50th,75th,90th,and 95th percentiles of ADC values and ADCmin,ADCmax,kurtosis,skewness,and entropy were obtained from the volumetric histogram analysis.Comparisons were made by an independent Student's t-test or Mann-Whitney U test.Multiple-class receiver operating characteristic curve analysis was performed to determine and compare the diagnostic value of each significant parameter.RESULTS In total,40 patients with histopathologically confirmed IPAC(n=17)or PPAC(n=23)were enrolled.The mean,5th,25th,50th,75th,90th,and 95th percentiles and ADCmax derived from ADC1000 were significantly lower in the PPAC group than in the IPAC group(P<0.05).However,values derived from ADC800 showed no significant difference between the two groups.The 75th percentile of ADC1000 values achieved the highest area under the curve(AUC)for differentiating IPAC from PPAC(AUC=0.781;sensitivity,91%;specificity,59%;cut-off value,1.50×10^-3 mm^2/s).CONCLUSION Volumetric ADC histogram analysis at a b-value of 1000 s/mm2 might be helpful for differentiating the histological subtypes of periampullary adenocarcinoma before surgery.

Histogram analysis of apparent diffusion coefficient predicts response to radiofrequency ablation in hepatocellular carcinoma

Objective: The aim of this study was to predict tumor progression in patients with hepatocellular carcinoma(HCC) treated with radiofrequency ablation(RFA) using histogram analysis of apparent diffusion coefficients(ADC).Methods: Breath-hold diffusion weighted imaging(DWI) was performed in 64 patients(33 progressive and 31 stable) with biopsy-proven HCC prior to RFA. All patients had pre-treatment magnetic resonance imaging(MRI)and follow-up computed tomography(CT) or MRI. The ADC values(ADC_(10), ADC_(30_, ADC_(median) and ADC_(max))were obtained from the histogram's 10 th, 30 th, 50 th and 100 th percentiles. The ratios of ADC_(10), ADC_(30_,ADCmedian and ADCmax to the mean non-lesion area-ADC(RADC_(10), RADC_(30_, RADC_(median), and RADC_(max)) were calculated. The two patient groups were compared. Key predictive factors for survival were determined using the univariate and multivariate analysis of the Cox model. The Kaplan-Meier survival analysis was performed, and pairs of survival curves based on the key factors were compared using the log-rank test.Results: The ADC_(30_, ADCmedian, ADCmax, RADC_(30_, RADC_(median), and RADC_(max) were significantly larger in the progressive group than in the stable group(P<0.05). The median progression-free survival(PFS) was 22.9 months for all patients. The mean PFS for the stable and progressive groups were 47.7±1.3 and 9.8±1.3 months,respectively. Univariate analysis indicated that RADC_(10), RADC_(30_, and RADC_(median) were significantly correlated with the PFS [hazard ratio(HR)=31.02, 43.84, and 44.29, respectively, P<0.05 for all]. Multivariate analysis showed that RADCmedian was the only independent predictor of tumor progression(P=0.04). And the cutoff value of RADC_(median) was 0.71.Conclusions: Pre-RFA ADC histogram analysis might serve as a useful biomarker for predicting tumor progression and survival in patients with HCC treated with RFA.

Infrared image segmentation method based on 2D histogram shape modification and optimal objective function

In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

Novel histogram descriptor for global feature extraction and description

A novel histogram descriptor for global feature extraction and description was presented. Three elementary primitives for a 2×2 pixel grid were defined. The complex primitives were computed by matrix transforms. These primitives and equivalence class were used for an image to compute the feature image that consisted of three elementary primitives. Histogram was used for the transformed image to extract and describe the features. Furthermore, comparisons were made among the novel histogram descriptor, the gray histogram and the edge histogram with regard to feature vector dimension and retrieval performance. The experimental results show that the novel histogram can not only reduce the effect of noise and illumination change, but also compute the feature vector of lower dimension. Furthermore, the system using the novel histogram has better retrieval performance.

The Use of Rank Histograms and MVL Diagrams to Characterize Ensemble Evolution in Weather Forecasting

Rank Histograms are suitable tools to assess the quality of ensembles within an ensemble prediction system or framework. By counting the rank of a given variable in the ensemble, we are basically making a sample analysis, which does not allow us to distinguish if the origin of its variability is external noise or comes from chaotic sources. The recently introduced Mean to Variance Logarithmic （MVL） Diagram accounts for the spatial variability, being very sensitive to the spatial localization produced by infinitesimal perturbations of spatiotemporal chaotic systems. By using as a benchmark a simple model subject to noise, we show the distinct information given by Rank Histograms and MVL Diagrams. Hence, the main effects of the external noise can be visualized in a graphic. From the MVL diagram we clearly observe a reduction of the amplitude growth rate and of the spatial localization （chaos suppression）, while from the Rank Histogram we observe changes in the reliability of the ensemble. We conclude that in a complex framework including spatiotemporal chaos and noise, both provide a more complete forecasting picture.

An Improved Double-Threshold Method Based on Gradient Histogram

This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better. Then an improved double-threshold method is proposed, which is combined with the method of maximum classes variance, estimating-area method and double-threshold method. This method can automatically select two different thresholds to segment gradient images. The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result. Key words gradient histogram image - threshold selection - double-threshold method - maximum classes variance method CLC number TP 391. 41 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and the Project of Chenguang Plan in Wuhan (985003062)Biography: YANG Shen (1977-), female, Ph. D. candidate, research direction: multimedia information processing and network technology.

Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram modification.The most in JPEG bitstream consists of a sequence of VLCs(variable length codes)and the appended bits.Each VLC has a corresponding RLV(run/length value)to record the AC/DC coefficients.To achieve lossless data hiding with high payload,we shift the histogram of VLCs and modify the DHT segment to embed data.Since we sort the histogram of VLCs in descending order,the filesize expansion is limited.The paper’s key contribution includes:Lossless data hiding,less filesize expansion in identical pay-load and higher embedding efficiency.