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.

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.

P-tile与直方图模糊C-均值聚类相结合的路面图像阈值分割

路面图像裂缝自动检测技术是公路养护技术的重要方向,路面图像的分割是路面图像处理的关键步骤。由于噪声等干扰因素的影响,使得利用传统的模糊C-均值聚类(FCM)算法进行路面图像分割得不到满意的结果。本文采用P-tile算法和直方图模糊C-均值聚类算法对路面图像进行分割,一方面克服了传统FCM运算量大、计算速度慢的缺点,另一方面减少分割算法分析的范围,增强了分割的效果。实验证明,本文算法能较好地分割出路面图像的裂缝。

P-tile与直方图FCM结合的路面图像分块分割

公路养护需要用到路面图像裂缝自动检测技术,路面图像的分割是路面图像处理的关键。由于噪声等干扰因素的影响,以往利用传统的模糊C-均值聚类(FCM)算法进行路面图像分割得不到满意的结果。文章采用P-tile算法和直方图模糊C-均值聚类算法对路面图像进行分块阈值分割,既克服了传统FCM运算量大,计算速度慢的缺点,又可减少分割算法的分析范围;而且,因不同的子图有不同的阈值,可避免统一阈值的缺陷,使图像分割更加准确。实验证明,该算法能较好地分割出路面图像的裂缝。

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.

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.

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.

Content-based Image Retrieval Using Color Histogram

This paper introduces the principles of using color histogram to match images in CBIR. And a prototype CBIR system is designed with color matching function. A new method using 2-dimensional color histogram based on hue and saturation to extract and represent color information of an image is presented. We also improve the Euclidean-distance algorithm by adding Center of Color to it. The experiment shows modifications made to Euclidean-distance signif-icantly elevate the quality and efficiency of retrieval.

Hand segmentation from a single depth image based on histogram threshold selection and shallow CNN

Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).

Spatial geometric constraints histogram descriptors based on curvature mesh graph for 3D pollen particles recognition

This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.

Enhancement Technique of Image Contrast using New Histogram Transformation

This paper presents a preprocessing technique that can provide the improved quality of image robust to illumination changes. First, in order to enhance the image contrast, we proposed new adaptive histogram transformation combining histogram equalization and histogram specification. Here, by examining the characteristic of histogram distribution shape, we determine the appropriate target distribution. Next, applying the histogram equalization with an image histogram, we have obtained the uniform distribution of pixel values, and then we have again carried out the histogram transformation using an inverse of target distribution function. Finally we have conducted various experiments that can enhance the quality of image by applying our method with various standard images. The experimental results show that the proposed method can achieve moderately good image enhancement results.

Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer

BACKGROUND Whole-tumor apparent diffusion coefficient(ADC)histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy(nCRT)response in patients with locally advanced rectal cancer(LARC).AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.METHODS This is a single-center,retrospective study,which included 48 patients with LARC.All patients underwent a pre-treatment magnetic resonance imaging(MRI)scan for primary tumor staging and a second restaging MRI for response evaluation.The sample was distributed as follows:18 responder patients(R)and 30 non-responders(non-R).Eight parameters derived from the whole-lesion histogram analysis(ADCmean,skewness,kurtosis,and ADC10^(th),25^(th),50^(th),75^(th),90^(th) percentiles),as well as the ADCmean from the hot spot region of interest(ROI),were calculated for each patient before and after treatment.Then all data were compared between R and non-R using the Mann-Whitney U test.Two measures of diagnostic accuracy were applied:the receiver operating characteristic curve and the diagnostic odds ratio(DOR).We also reported intra-and interobserver variability by calculating the intraclass correlation coefficient(ICC).RESULTS Post-nCRT kurtosis,as well as post-nCRT skewness,were significantly lower in R than in non-R(both P<0.001,respectively).We also found that,after treatment,R had a larger loss of both kurtosis and skewness than non-R(Δ%kurtosis and Δ skewness,P<0.001).Other parameters that demonstrated changes between groups were post-nCRT ADC10^(th),Δ%ADC10^(th),Δ%ADCmean,and ROIΔ%ADCmean.However,the best diagnostic performance was achieved byΔ%kurtosis at a threshold of 11.85%(Area under the receiver operating characteristic curve[AUC]=0.991,DOR=376),followed by post-nCRT kurtosis=0.78×10^(-3)mm^(2)/s(AUC=0.985,DOR=375.3),Δskewness=0.16(AUC=0.885,DOR=192.2)and post-nCRT skewness=1.59×10^(-3)mm^(2)/s(AUC=0.815,DOR=168.6).Finally,intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement,ensuring the implementation of histogram analysis into routine clinical practice.CONCLUSION Whole-tumor ADC histogram parameters,particularly kurtosis and skewness,are relevant biomarkers for predicting the nCRT response in LARC.Both parameters appear to be more reliable than ADCmean from one-slice ROI.