An in-pixel histogramming time-to-digital converter(hTDC)based on octonary search and 4-tap phase detection is presented,aiming to improve frame rate while ensuring high precicion.The proposed hTDC is a 12-bit two-ste...An in-pixel histogramming time-to-digital converter(hTDC)based on octonary search and 4-tap phase detection is presented,aiming to improve frame rate while ensuring high precicion.The proposed hTDC is a 12-bit two-step converter consisting of a 6-bit coarse quantization and a 6-bit fine quantization,which supports a time resolution of 120 ps and multiphoton counting up to 2 GHz without a GHz reference frequency.The proposed hTDC is designed in 0.11μm CMOS process with an area consumption of 6900μm^(2).The data from a behavioral-level model is imported into the designed hTDC circuit for simulation verification.The post-simulation results show that the proposed hTDC achieves 0.8%depth precision in 9 m range for short-range system design specifications and 0.2%depth precision in 48 m range for long-range system design specifications.Under 30×10^(3) lux background light conditions,the proposed hTDC can be used for SPAD-based flash LiDAR sensor to achieve a frame rate to 40 fps with 200 ps resolution in 9 m range.展开更多
Early detection of Forest and Land Fires(FLF)is essential to prevent the rapid spread of fire as well as minimize environmental damage.However,accurate detection under real-world conditions,such as low light,haze,and ...Early detection of Forest and Land Fires(FLF)is essential to prevent the rapid spread of fire as well as minimize environmental damage.However,accurate detection under real-world conditions,such as low light,haze,and complex backgrounds,remains a challenge for computer vision systems.This study evaluates the impact of three image enhancement techniques—Histogram Equalization(HE),Contrast Limited Adaptive Histogram Equalization(CLAHE),and a hybrid method called DBST-LCM CLAHE—on the performance of the YOLOv11 object detection model in identifying fires and smoke.The D-Fire dataset,consisting of 21,527 annotated images captured under diverse environmental scenarios and illumination levels,was used to train and evaluate the model.Each enhancement method was applied to the dataset before training.Model performance was assessed using multiple metrics,including Precision,Recall,mean Average Precision at 50%IoU(mAP50),F1-score,and visual inspection through bounding box results.Experimental results show that all three enhancement techniques improved detection performance.HE yielded the highest mAP50 score of 0.771,along with a balanced precision of 0.784 and recall of 0.703,demonstrating strong generalization across different conditions.DBST-LCM CLAHE achieved the highest Precision score of 79%,effectively reducing false positives,particularly in scenes with dispersed smoke or complex textures.CLAHE,with slightly lower overall metrics,contributed to improved local feature detection.Each technique showed distinct advantages:HE enhanced global contrast;CLAHE improved local structure visibility;and DBST-LCM CLAHE provided an optimal balance through dynamic block sizing and local contrast preservation.These results underline the importance of selecting preprocessing methods according to detection priorities,such as minimizing false alarms or maximizing completeness.This research does not propose a new model architecture but rather benchmarks a recent lightweight detector,YOLOv11,combined with image enhancement strategies for practical deployment in FLF monitoring.The findings support the integration of preprocessing techniques to improve detection accuracy,offering a foundation for real-time FLF detection systems on edge devices or drones,particularly in regions like Indonesia.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
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 a...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.展开更多
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...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.展开更多
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 weake...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.展开更多
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 ...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.展开更多
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(AD...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.展开更多
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 progno...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.展开更多
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, the...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.展开更多
Atomic force microscopy（AFM） is a commonly used technique for graphene thickness measurement.However, due to surface roughness caused by graphene itself and variation introduced in AFM measurement, graphene thicknes...Atomic force microscopy（AFM） is a commonly used technique for graphene thickness measurement.However, due to surface roughness caused by graphene itself and variation introduced in AFM measurement, graphene thickness is difficult to be accurately determined by AFM. In this paper, a histogram method was used for reliable measurements of graphene thickness using AFM. The influences of various measurement parameters in AFM analysis were investigated. The experimental results indicate that significant deviation can be introduced using various order of flatten and improperly selected measurement parameters including amplitude setpoint and drive amplitude. At amplitude setpoint of 100 mV and drive amplitude of 100 m V, thickness of 1 layer（1L）, 2 layers（2L） and 4 layers（4L） graphene were measured.The height differences for 1L, 2L and 4L were 1.51 ± 0.16 nm, 1.92 ± 0.13 nm and 2.73 ± 0.10 nm, respectively. By comparing these values, thickness of single layer graphene can be accurately determined to be0.41 ± 0.09 nm.展开更多
This study was to investigate the diagnostic value of the computed tomography（CT） histogram in thyroid benign solitary coarse calcification nodules（BSCNs）. A total of 89 thyroid solitary coarse calcification nodu...This study was to investigate the diagnostic value of the computed tomography（CT） histogram in thyroid benign solitary coarse calcification nodules（BSCNs）. A total of 89 thyroid solitary coarse calcification nodules（coarse calcification ≥5 mm, no definite soft tissue around calcification） confirmed either by surgery or histopathological examination in 86 cases enrolled from January 2009 to December 2015 were evaluated. These included 56 BSCNs and 33 malignant solitary coarse calcification nodules（MSCNs）. Overall, 27 cut-off values were calculated by N（4≤N≤30） times of 50 Hounsfield units（HU） in the range of 200 to 1500 HU, and each cut-off value and the differences in the corresponding area percentages in the CT histogram were recorded for BSCN and MSCN. The optimal cut-off value and the corresponding area percentage were established by receiver operating characteristic（ROC） curve analysis. In the 19 groups with an ROC area under curve（AUC） of more than 0.7, at a cut-off value of 800 HU and at an area percentage of no more than 93.8%, the ROC AUC reached the maximum of 0.79, and the accuracy, sensitivity, and specificity were 75.3%, 80.4%, and 66.7%, respectively. At a cut-off value of 1050 HU and at an area percentage of no more than 93.6%, the accuracy, sensitivity, and specificity were 71.9%, 60.7%, and 90.9%, respectively. At a cut-off of 1150 HU and area of no more than 98.4%, the accuracy, sensitivity, and specificity were 70.8%, 57.1%, and 93.9%, respectively. At a cut-off of 600 HU and area of no more than 12.1%, the accuracy, sensitivity, and specificity were 61.8%, 39.3%, and 100.0%, respectively. Compared with the cut-off value of 800 HU and an area percentage of no more than 93.8%, the sensitivity of cut-off values and minimum areas of 1050 HU and 93.6%, of 1150 HU and 98.4%, and of 600 HU and 12.1%, was gradually decreasing; however, their specificity was gradually increasing. This can provide an important basis for reducing the misdiagnosis and unnecessary surgical trauma.展开更多
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 ...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.展开更多
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 analy...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.展开更多
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. ...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.展开更多
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 V...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.展开更多
The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image...The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.展开更多
基金National Key Research and Development Program of China(2022YFB2804401)。
文摘An in-pixel histogramming time-to-digital converter(hTDC)based on octonary search and 4-tap phase detection is presented,aiming to improve frame rate while ensuring high precicion.The proposed hTDC is a 12-bit two-step converter consisting of a 6-bit coarse quantization and a 6-bit fine quantization,which supports a time resolution of 120 ps and multiphoton counting up to 2 GHz without a GHz reference frequency.The proposed hTDC is designed in 0.11μm CMOS process with an area consumption of 6900μm^(2).The data from a behavioral-level model is imported into the designed hTDC circuit for simulation verification.The post-simulation results show that the proposed hTDC achieves 0.8%depth precision in 9 m range for short-range system design specifications and 0.2%depth precision in 48 m range for long-range system design specifications.Under 30×10^(3) lux background light conditions,the proposed hTDC can be used for SPAD-based flash LiDAR sensor to achieve a frame rate to 40 fps with 200 ps resolution in 9 m range.
基金funded by the Directorate of Research,Technology,and Community Service,Ministry of Higher Education,Science,and Technology of the Republic of Indonesia the Regular Fundamental Research scheme,with grant numbers 001/LL6/PL/AL.04/2025,011/SPK-PFR/RIK/05/2025.
文摘Early detection of Forest and Land Fires(FLF)is essential to prevent the rapid spread of fire as well as minimize environmental damage.However,accurate detection under real-world conditions,such as low light,haze,and complex backgrounds,remains a challenge for computer vision systems.This study evaluates the impact of three image enhancement techniques—Histogram Equalization(HE),Contrast Limited Adaptive Histogram Equalization(CLAHE),and a hybrid method called DBST-LCM CLAHE—on the performance of the YOLOv11 object detection model in identifying fires and smoke.The D-Fire dataset,consisting of 21,527 annotated images captured under diverse environmental scenarios and illumination levels,was used to train and evaluate the model.Each enhancement method was applied to the dataset before training.Model performance was assessed using multiple metrics,including Precision,Recall,mean Average Precision at 50%IoU(mAP50),F1-score,and visual inspection through bounding box results.Experimental results show that all three enhancement techniques improved detection performance.HE yielded the highest mAP50 score of 0.771,along with a balanced precision of 0.784 and recall of 0.703,demonstrating strong generalization across different conditions.DBST-LCM CLAHE achieved the highest Precision score of 79%,effectively reducing false positives,particularly in scenes with dispersed smoke or complex textures.CLAHE,with slightly lower overall metrics,contributed to improved local feature detection.Each technique showed distinct advantages:HE enhanced global contrast;CLAHE improved local structure visibility;and DBST-LCM CLAHE provided an optimal balance through dynamic block sizing and local contrast preservation.These results underline the importance of selecting preprocessing methods according to detection priorities,such as minimizing false alarms or maximizing completeness.This research does not propose a new model architecture but rather benchmarks a recent lightweight detector,YOLOv11,combined with image enhancement strategies for practical deployment in FLF monitoring.The findings support the integration of preprocessing techniques to improve detection accuracy,offering a foundation for real-time FLF detection systems on edge devices or drones,particularly in regions like Indonesia.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
基金sponsored by the National Science&Technology Major Special Project(Grant No.2011ZX05025-001-04)
文摘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.
基金sponsored by Important National Science and Technology Specific Projects of China (Grant No.2008ZX05023-005-011 and No. 2008ZX05040-003)the National 973 Program of China (Grant No. 2006CB202208)
文摘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.
基金National Natural Science Foundation of China(No.61201412)
文摘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.
文摘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.
基金supported by CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2016-I2M-1-001)PUMC Youth Fund (No. 2017320010)Beijing Hope Run Fund of Cancer Foundation of China (No. LC2016B15)
文摘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.
基金Supported by the National Natural Science Foundation of China,No.81701657,No.81571642,No.81801695,and No.81771801the Fundamental Research Funds for the Central Universities,No.2017KFYXJJ126
文摘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.
基金supported by the China Postdoctoral Science Foundation(20100471451)the Science and Technology Foundation of State Key Laboratory of Underwater Measurement&Control Technology(9140C2603051003)
文摘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.
基金supported by the Program of National Key Technology R&D Program of the Ministry of Science and Technology of China(2011BAK15B04)
文摘Atomic force microscopy（AFM） is a commonly used technique for graphene thickness measurement.However, due to surface roughness caused by graphene itself and variation introduced in AFM measurement, graphene thickness is difficult to be accurately determined by AFM. In this paper, a histogram method was used for reliable measurements of graphene thickness using AFM. The influences of various measurement parameters in AFM analysis were investigated. The experimental results indicate that significant deviation can be introduced using various order of flatten and improperly selected measurement parameters including amplitude setpoint and drive amplitude. At amplitude setpoint of 100 mV and drive amplitude of 100 m V, thickness of 1 layer（1L）, 2 layers（2L） and 4 layers（4L） graphene were measured.The height differences for 1L, 2L and 4L were 1.51 ± 0.16 nm, 1.92 ± 0.13 nm and 2.73 ± 0.10 nm, respectively. By comparing these values, thickness of single layer graphene can be accurately determined to be0.41 ± 0.09 nm.
文摘This study was to investigate the diagnostic value of the computed tomography（CT） histogram in thyroid benign solitary coarse calcification nodules（BSCNs）. A total of 89 thyroid solitary coarse calcification nodules（coarse calcification ≥5 mm, no definite soft tissue around calcification） confirmed either by surgery or histopathological examination in 86 cases enrolled from January 2009 to December 2015 were evaluated. These included 56 BSCNs and 33 malignant solitary coarse calcification nodules（MSCNs）. Overall, 27 cut-off values were calculated by N（4≤N≤30） times of 50 Hounsfield units（HU） in the range of 200 to 1500 HU, and each cut-off value and the differences in the corresponding area percentages in the CT histogram were recorded for BSCN and MSCN. The optimal cut-off value and the corresponding area percentage were established by receiver operating characteristic（ROC） curve analysis. In the 19 groups with an ROC area under curve（AUC） of more than 0.7, at a cut-off value of 800 HU and at an area percentage of no more than 93.8%, the ROC AUC reached the maximum of 0.79, and the accuracy, sensitivity, and specificity were 75.3%, 80.4%, and 66.7%, respectively. At a cut-off value of 1050 HU and at an area percentage of no more than 93.6%, the accuracy, sensitivity, and specificity were 71.9%, 60.7%, and 90.9%, respectively. At a cut-off of 1150 HU and area of no more than 98.4%, the accuracy, sensitivity, and specificity were 70.8%, 57.1%, and 93.9%, respectively. At a cut-off of 600 HU and area of no more than 12.1%, the accuracy, sensitivity, and specificity were 61.8%, 39.3%, and 100.0%, respectively. Compared with the cut-off value of 800 HU and an area percentage of no more than 93.8%, the sensitivity of cut-off values and minimum areas of 1050 HU and 93.6%, of 1150 HU and 98.4%, and of 600 HU and 12.1%, was gradually decreasing; however, their specificity was gradually increasing. This can provide an important basis for reducing the misdiagnosis and unnecessary surgical trauma.
文摘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.
基金support from MEC,Spain,through Grant No.CGL2007-64387/CLIthe AECID,Spain,for support through projects A/013666/07 and A/018685/08
文摘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.
基金Project(60873010) supported by the National Natural Science Foundation of ChinaProjects(N090504005, N090604012, N090104001) supported by the Fundamental Research Funds for the Central UniversitiesProject(NCET-05-0288) supported by Program for New Century Excellent Talents in University
文摘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.
基金This research work is partly supported by National Natural Science Foundation of China(61502009,61525203,61472235,U1636206,61572308)CSC Postdoctoral Project(201706505004)+2 种基金Anhui Provincial Natural Science Foundation(1508085SQF216)Key Program for Excellent Young Talents in Colleges and Universities of Anhui Province(gxyqZD2016011)Anhui university research and innovation training project for undergraduate students.
文摘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.
基金Supported by the National Natural Science Foundation of China under Grant No.50909025/E091002the Open Research Foundation of SKLab AUV, HEU under Grant No.2008003
文摘The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.
