Incorporating GSA-SPECT into CT-based dose-volume histograms for advanced hepatocellular carcinoma radiotherapy

In single photon emission computed tomography-based three-dimensional radiotherapy(SPECT-B-3DCRT), im-ages of Tc-99 m galactosyl human serum albumin(GSA), which bind to receptors on functional liver cells, are merged with the computed tomography simulation im-ages. Functional liver is defined as the area of normal liver where GSA accumulation exceeds that of hepato-cellular carcinoma(HCC). In cirrhotic patients with a gigantic, proton-beam-untreatable HCC of ≥ 14 cm in diameter, the use of SPECT-B-3DCRT in combination with transcatheter arterial chemoembolization achieved a 2-year local tumor control rate of 78.6% and a 2-year survival rate of 33.3%. SPECT-B-3DCRT was applied to HCC to preserve as much functional liver as possible. Sixty-four patients with HCC, including 30 with Child B liver cirrhosis, received SPECT-B-3DCRT and none ex-perienced fatal radiation-induced liver disease(RILD). The Child-Pugh score deteriorated by 1 or 2 in > 20% of functional liver volume that was irradiated with ≥ 20 Gy. The deterioration in the Child-Pugh score decreased when the radiation plan was designed to irradiate ≤ 20% of the functional liver volume in patients givendoses of ≥ 20 Gy(FLV20Gy). Therefore, FLV20 Gy ≤ 20% may represent a safety index to prevent RILD during 3DCRT for HCC. To supplement FLV20 Gy as a qualitative index, we propose a quantitative indicator, F 20 Gy, which was calculated as F 20 Gy = 100% ×(the GSA count in the area irradiated with ≥ 20 Gy)/(the GSA count in the whole liver).

Symptomatic Radiation Pneumonitis in NSCLC Patients Receiving EGFR-TKIs and Concurrent Oncedaily Thoracic Radiotherapy:Predicting the Value of Clinical and Dose-volume Histogram Parameters

Background and objectives:The incidence of symptomatic radiation pneumonitis(RP)and its relationship with dose-volume histogram(DVH)parameters in non-small cell lung cancer(NSCLC)patients receiving epidermal growth factor receptortyrosine kinase inhibitors(EGFR-TKIs)and concurrent once-daily thoracic radiotherapy(TRT)remain unclear.We aim to analyze the values of clinical factors and dose-volume histogram(DVH)parameters to predict the risk for symptomatic RP in these patients.Methods:Between 2011 and 2019,we retrospectively analyzed and identified 85 patients who had received EGFR-TKIs and oncedaily TRT simultaneously(EGFR-TKIs group)and 129 patients who had received concurrent chemoradiotherapy(CCRT group).The symptomatic RP was recorded according to the Common Terminology Criteria for Adverse Event(CTCAE)criteria(grade 2 or above).Statistical analyses were performed using SPSS 26.0.Results:In total,the incidences of symptomatic(grade≥2)and severe RP(grade≥3)were 43.5%(37/85)and 16.5%(14/85)in EGFR-TKIs group vs 27.1%(35/129)and 10.1%(13/129)in CCRT group respectively.After 1:1 ratio between EGFR-TKIs group and CCRT group was matched by propensity score matching,chi-square test suggested that the incidence of symptomatic RP in the MATCHED EGFR-TKIs group was higher than that in the matched CCRT group(χ^(2)=4.469,P=0.035).In EGFRTKIs group,univariate and multivariate analyses indicated that the percentage of ipsilateral lung volume receiving≥30 Gy(ilV_(30))[odds ratio(OR):1.163,95%CI:1.036-1.306,P=0.011]and the percentage of total lung volume receiving≥20 Gy(tlV_(20))(OR:1.171,95%CI:1.031-1.330,P=0.015),with chronic obstructive pulmonary disease(COPD)or not(OR:0.158,95%CI:0.041-0.600,P=0.007),were independent predictors of symptomatic RP.Compared to patients with lower iIV_(30)/tlV_(20)values(ilV_(30)and tlV_(20)<cut-off point values)and without COPD,patients with higher ilV_(30)/tlV_(20)values(ilV_(30)and tlV_(20)>cut-off point values)and COPD had a significantly higher risk for developing symptomatic RP,with a hazard ratio(HR)of 1.350(95%CI:1.190-1.531,P<0.001).Conclusion:Patients receiving both EGFR-TKIs and once-daily TRT were more likely to develop symptomatic RP than patients receiving concurrent chemoradiotherapy.The ilV_(30),tlV_(20),and comorbidity of COPD may predict the risk of symptomatic RP among NSCLC patients receiving EGFR-TKIs and conventionally fractionated TRT concurrently.

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.

Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms

Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed;among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented;the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation.

Dose-volume parameters and clinical outcome of CT-guided free-hand high-dose-rate interstitial brachytherapy for cervical cancer

Currently, image-based 3-dimentional (3D) planning brachytherapy allows for a better assessment of gross tumor volume (GTV) and the definition and delineation of target volume in cervix cancer. In this study, we investigated the feasibility of our novel computed tomography (CT)-guided free-hand high-dose- rate interstitial brachytherapy (HDRISBT) technique for cervical cancer by evaluating the dosimetry and preliminary clinical outcome of this approach. Dose-volume histogram (DVH) parameters were analyzed according to the Gynecological GEC-ESTRO Working Group recommendations for image-based 3D treatment in cervical cancer. Twenty cervical cancer patients who underwent CT-guided free-hand HDRISBT between March 2009 and June 2010 were studied. With a median of 5 (range, 4-7) implanted needles for each patient, the median dose of brachytherapy alone delivered to 90% of the target volume (D90 ) was 45 (range, 33-54) Gyα/β10 for high-risk clinical target volume (HR-CTV) and 30 (range, 20-36) Gyα/β10 for intermediate-risk clinical target volume (IR-CTV). The percentage of the CTV covered by the prescribed dose (V100 ) of HR-CTV with brachytherapy alone was 81.9%-99.2% (median, 96.7%). With an additional dose of external beam radiotherapy (EBRT), the median D90 was 94 (range, 83-104) Gyα/β10 for HR-CTV and 77 (range, 70 -87) Gyα/β10 for IR-CTV; the median dose delivered to 100% of the target volume (D100 ) was 75 (range, 66-84) Gyα/β10 for HR-CTV and 65 (range, 57-73) Gyα/β10 for IR-CTV. The minimum dose to the most irradiated 2 cc volume (D2cc ) was 73-96 (median, 83) Gyα/β3 for the bladder, 64-98 (median, 73) Gyα/β3 for the rectum, and 52-69 (median, 61) Gyα/β3 for the sigmoid colon. After a median follow-up of 15 months (range, 3 -24 months), two patients experienced local failure, and 1 showed internal iliac nodal metastasis. Despite the relatively small number of needles used, CT-guided HDRISBT for cervical cancer showed favorable DVH parameters and clinical outcome.

High Dose-Volume SBRT Following TACE Improves Clinical Outcomes of Patients with Unresectable Hepatocellular Carcinoma

This study aimed to investigate the dose-volume effects of γ-ray stereotactic body radiotherapy (SBRT) on clinical outcomes of patients with huge-size (≥10 cm) unresectable hepatocellular carcinoma (HCC). A total of 59 patients with huge-size unresectable HCC were treated with SBRT following TACE between May 2006 and Dec. 2009. The analyzed parameters included fractional dose, marginal dose, maximal dose, and mean dose that the target received, as well as percentages of tumor volume encompassed by 60% (P60), 70% (P70), and 80% (P80) of isodose curves in entire tumor. The clinical outcomes included objective response rate (ORR), disease-free survival (DFS), overall survival (OS), and adverse event (AE). During median follow-up of 18.4 months, 81.4% of ORR (8.5% CR and 72.9% PR) was achieved, higher than 28.9% of ORR recently reported for TACE alone. 1- and 3-year DFS rates were 31.1% and 2.6% with median DFS of 8.7 months;1-, 3-, and 5-year OS rates were 46.5%, 13.7%, and 2.9%, with median OS of 11.8 months. P70 was the only factor significantly correlating to DFS (P = 0.009) and OS (P = 0.01). Neither severe radiation-related liver disease nor > grade 3 AE was observed. In conclusion, SBRT was a safe and effective option for treatment of huge-size unresectable HCC. P70 represented a parameter for predicting DFS and OS, and high dose-volume (e.g., P70) might be required to achieve improved clinical outcomes of patients with this type of HCC.

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval(CBIR)techniques have been widely deployed in many applications for seeking the abundant information existed in images.Due to large amounts of storage and computational requirements of CBIR,outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices.However,owing to the private content contained in images,directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem,so the images should be protected carefully before outsourcing.This paper presents a secure retrieval scheme for the encrypted images in the YUV color space.With this scheme,the discrete cosine transform(DCT)is performed on the Y component.The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling.Then the image owner transmits the encrypted images to the cloud server.When receiving a query trapdoor form on query user,the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components.The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms.Finally,the encrypted images closest to the query image are returned to the query user.

Underwater sonar image recognition based on gray-spatial histograms

A new gray-spatial histogram is proposed, which incorporates spatial informatio n with gray compositions without sacrificing the robustness of traditional gray histograms. The purpose is to consider the representation role of gray compositi ons and spatial information simultaneously. Each entry in the gray-spatial hist ogram is the gray frequency and corresponding position information of images. In the experiments of sonar image recognition, the results show that the gray-spa tial histogram is effective in practical use.

Facial expression recognition with contextualized histograms

A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.

An Automatic Approach for Satisfying Dose-Volume Constraints in Linear Fluence Map Optimization for IMPT

Prescriptions for radiation therapy are given in terms of dose-volume constraints (DVCs). Solving the fluence map optimization (FMO) problem while satisfying DVCs often requires a tedious trial-and-error for selecting appropriate dose control parameters on various organs. In this paper, we propose an iterative approach to satisfy DVCs using a multi-objective linear programming (LP) model for solving beamlet intensities. This algorithm, starting from arbitrary initial parameter values, gradually updates the values through an iterative solution process toward optimal solution. This method finds appropriate parameter values through the trade-off between OAR sparing and target coverage to improve the solution. We compared the plan quality and the satisfaction of the DVCs by the proposed algorithm with two nonlinear approaches: a nonlinear FMO model solved by using the L-BFGS algorithm and another approach solved by a commercial treatment planning system (Eclipse 8.9). We retrospectively selected from our institutional database five patients with lung cancer and one patient with prostate cancer for this study. Numerical results show that our approach successfully improved target coverage to meet the DVCs, while trying to keep corresponding OAR DVCs satisfied. The LBFGS algorithm for solving the nonlinear FMO model successfully satisfied the DVCs in three out of five test cases. However, there is no recourse in the nonlinear FMO model for correcting unsatisfied DVCs other than manually changing some parameter values through trial and error to derive a solution that more closely meets the DVC requirements. The LP-based heuristic algorithm outperformed the current treatment planning system in terms of DVC satisfaction. A major strength of the LP-based heuristic approach is that it is not sensitive to the starting condition.

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.

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.