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.

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).

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.

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.

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.

Noninvasive Fetal Lung Maturity Prediction Based on Amniotic Fluid Turbidity Using Ultrasonic Histogram Measurement Function

Background: Amniotic fluid turbidity increases with fetal lung maturation due to vernix and lung surfactant micelles suspended in the amniotic fluid. This study focused on this phenomenon and evaluated the presence or absence of respiratory distress syndrome (RDS)/transient tachypnea of the newborn (TTN) by quantitatively assessing the brightness of the amniotic fluid turbidity using a noninvasive ultrasound histogram measurement function. Methods: We included cases of singleton pregnancies managed at the Niigata University Medical and Dental Hospital between November 2020 and March 2022. Histograms of amniotic fluid turbidity were measured at the center of the amniotic fluid depth, avoiding the fetus, placenta, and umbilical cord, with the gain setting set to 0, and the average value was obtained after three measurements. Histograms of fetal urine in the bladder were measured similarly. The value obtained by subtracting the fetal bladder brightness value from the amniotic brightness value based on histogram measurements was used as the final amniotic fluid brightness value. Results: We included 118 cases (16 of RDS/TTN and 102 of control). The gestational age of delivery weeks was correlated with amniotic fluid brightness (Spearman’s rank correlation coefficient was 0.344;p = 0.00014). Amniotic fluid brightness values were significantly lower in the RDS/TTN group than in the control group (RDS/TTN: 16.2 ± 13.5, control: 26.3 ± 16.3;p = 0.020). The optimal cutoff value of amniotic fluid brightness to predict RDS/TTN was 20.3. For predicting RDS/TTN, the sensitivity, specificity, positive predictive value, and negative predictive value were 91.7%, 69.6%, 26.2%, and 94.1%, respectively. Conclusions: The quantitative value of the amniotic fluid brightness by histogram measurements may provide an easy and objective index for evaluating the presence or absence of RDS/TTN.

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape features.It exhibits a higher classification accuracy value of 99.6%when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

基于改进直方图均衡的SF6泄漏区域增强算法

SF6气体红外成像易受环境噪声影响,呈低对比度与低信噪比特性。导致现有算法无法自适应增强SF6泄漏区域和抑制高斯噪声。针对上述问题,提出一种基于改进HE的SF6泄漏区域增强算法。该算法首先采用单尺度Retinex处理原始SF6图像获得反射图像,然后利用引导滤波将反射图像分解为细节层和基本层,最后采用改进的直方图均衡化来自适应处理基本层,并将增强后的图像进行融合来获得最终的图像。实验结果表明:本文算法不仅能够自适应增强泄漏区域的对比度,而且具有良好边缘保持特性和抑制高斯噪声的性能。其增强效果优于现有的SF6红外图像增强算法。有效改善了SF6红外图像低对比度和低信噪比特性。

基于强噪声条件下的激光图像边缘检测研究

边缘检测是激光图像处理的一个重要步骤,针对当前激光图像边缘检测误差大,易受噪声干扰等缺陷,为了获得完整的激光图像边缘,设计了基于强噪声条件下的激光图像边缘检测方法。首先采集激光图像,采用直方图均衡技术对原始激光图像进行增强处理,改善激光图像质量,然后采用小波变换去除激光图像噪声,并采用Canny算子进行激光图像边缘检测,最后测试结果表明,与当前激光图像边缘检测方法相对比,本方法能够检测出更多激光图像边缘细节,激光图像边缘检测精度的平均值超过93%,而且激光图像边缘检测时间控制60 ms以内,为激光图像提供了一种高精度检测技术。

基于二重威布尔混合分布的某型燃机燃油泵组件可靠性分析

燃油泵组件是机电一体化的产品,其在使用中获得的故障数据存在不同失效机理的情况,因此一重威布尔分布无法准确描述产品失效机理,而混合分布通过将多种失效分布进行耦合,能够充分描述产品失效机理。本文将二重三参数威布尔混合分布模型应用在舰船发动机附件故障分析中,基于某型燃机燃油泵组件的故障数据,通过数据分析直方图确定故障模型与模型初始化参数,通过Matlab非线性曲线拟合方法来估计二重三参数威布尔混合分布的参数。研究方法及成果能够为燃油泵组件可靠性分析及优化改进提供一定的借鉴。

基于CLAHE-PCA的矿井低照度图像增强研究

地下矿山巷道环境往往面临光线不足,难以通过获取其暗通道图像判断岩体剥落等异常情况。针对矿井巷道暗通道图像对比度低的问题,提出了一种基于CLAHE-PCA的图像增强算法。首先使用CLAHE算法将获取的矿井巷道原始暗通道图像做对比度增强处理,然后使用自适应Gamma算法对亮度低的图像予以增加对比度矫正;将矫正后获得的灰度图转为RGB图像,通过PCA对其进行平滑处理,以便更多地还原暗通道图像的细节。以峰值信噪比、结构相似性、平均梯度和信息熵等作为评价指标,对试验结果进行验证。结果表明:该方法能够有效处理低对比度的矿井巷道图像,处理后的图像结构相似性达到93%,鲁棒性强,同时能够更多地还原图像的细节。

微积分引入概率密度研究的教学设计

从直方图分析入手,剖析非等距分组概率平均密度直方图以及相应函数的数学实质,为概率密度概念的确立提供了路径。用微积分推导了指数分布的分布函数,展现了获得概率分布的数学建模方法,为微积分引入概率密度研究的教学设计提供了参考。

基于Raspberry Pi的安全帽识别系统设计与实现

为了便于施工危险区域人员的自动化识别,提出了一种基于Raspberry Pi的安全帽识别算法。该算法将摄像头采集到的原始视频图像进行滤波、形态学等处理,再对图像中的安全帽进行识别。对于彩色安全帽,将原始图像转换到HSV空间,根据不同颜色色调阈值的设定同时识别红、黄、蓝三种颜色的安全帽,并结合形状特征剔除错误目标。对于白色安全帽,将原始图像转化成B通道下的灰度图像,解决了将黄色误检为白色的问题。采用V通道直方图均衡化的方法,提升了昏暗光线条件下的图像亮度。实验结果表明:在无需提前训练的情况下,算法对于单色安全帽识别准确率超过了91%,对于多色安全帽识别率超过了90%,为施工危险区域的安全隐患排查和作业管控提供了解决方案。

CT增强扫描灰度直方图纹理分析技术对肺癌患者术前恶性程度评估及对预后的预测价值

目的探究CT增强扫描灰度直方图纹理分析技术对肺癌患者术前恶性程度评估及对预后的预测价值。方法选择2018年5月至2022年9月在我院就诊的且经术后病理证实的肺癌患者580例作为研究对象。依据术后病理分期,将研究对象分为高分化组(183例)、中分化组(195例)、低分化组(202例)。根据预后情况,将其分为预后良好组(356例)和预后不良组(224例)。由2名医师提取患者CT增强扫描灰度直方图纹理参数。受试者工作特征(receiver operating characteristic,ROC)曲线分析特征参数对肺癌患者术前恶性程度的诊断效能及预后的预测价值。多因素Logistic回归分析预后不良的重要影响因素并构建人工神经网络模型。Pearson分析检验参数间的相关性。结果均值、10%分位、50%分位等参数在高、中、低分化三组中有显著差异。三个参数对评估肺癌患者恶性程度均具有一定的诊断效能,且三者联合诊断效能最佳。均值、10%分位、50%分位是预后不良的保护因素,三者联合预测肺癌患者的曲线AUC值大于各指标单独预测的AUC值。ROC曲线和累积增益图表明该模型预测能力良好。纹理特征参数中,69.44%的参数具有相关性。结论CT增强扫描灰度直方图纹理参数在一定程度上反应肺癌患者术前恶性程度信息,给术前预测以及预后提供了重要方法。

用于实时图像增强的灰度等间距密度均衡改进算法

实时图像的增强处理,除了保证单帧图像具有较高的对比度,还应考虑处理算法的实时性和图像序列间增强效果的连贯性。灰度等间距密度均衡算法通过去除冗余灰度级后进行有效灰度级等间距重排,可以有效增强图像对比度。本文以灰度等间距密度均衡增强算法为基础,对其有效灰度级有效阈值选取、过度增强等问题进行了相应改进,以满足连续图像序列的实时增强处理需求。实验结果表明,改进算法可以有效避免图像局部变化对整体增强效果的影响。对于1920×1080分辨率的图像,单帧处理时间约为32 ms。本改进算法对于连续图像序列具有良好的增强效果,且能够较好地满足实时图像增强对于连贯性、实时性、鲁棒性的处理需求。

增强MRI全域直方图鉴别儿童星形细胞瘤和室管膜瘤的价值

目的深入探究T_(1)全域灰度直方图分析在儿童后颅窝星形细胞瘤与室管膜瘤鉴别诊断中的实用价值。方法本研究对我院数据库中记录的51例经MRI检查及病理确诊的儿童后颅窝肿瘤病例进行了详尽的回顾性剖析。在这51例病例中,星形细胞瘤占26例(男性12例,女性14例),室管膜细胞瘤占25例(男性13例,女性12例)。患者年龄分布在1至12岁之间,平均年龄为(5.5±2.3)岁。我们运用Mazda软件,在两组MR增强T_(1)矢状位图像的每一层肿瘤层面上,精确地勾画出感兴趣区域,并进行全面的灰度全域直方图分析。接着,我们对这两组直方图参数特征进行了详尽的统计学对比,旨在深入剖析各参数在鉴别诊断中的统计意义,以期为儿童后颅窝肿瘤的精确诊断提供更为有效的辅助手段。结果在深入分析通过增强T_(1)全域灰度直方图所提取的九个参数时,我们发现其中四个参数——变异度(Variance)、偏度(Skewness)、第一百分位数(Perc.01%)以及第10百分位数(Perc.10%)在统计学上具有显著差异(P均<0.05),这一发现为我们的研究提供了强有力的数据支撑。特别是Variance这一参数,其灵敏度高达73.3%,特异度也达到了61.9%,且其曲线下面积(AUC)为0.731,展现出优秀的鉴别效能。进一步地,我们确定了Variance的最佳临界值为740.71,这一具体数值为我们的诊断提供了明确的参考标准。结论增强的T_(1)全域灰度直方图分析在儿童后颅窝星形细胞瘤和室管膜瘤的鉴别诊断中,展现出了不容忽视的价值。这一方法不仅能够为医生提供新的诊断视角,而且有望成为一种针对这两种儿童后颅窝肿瘤的有效辅助诊断工具,为我们的医疗实践带来积极的影响。

基于峰值直方图均衡化的车位图像增强

车位增强算法作为自动泊车的重要组成部分,其增强结果直接影响车位线提取效果。基于此,引入了暗通道作为低频分量进行自适应对比度增强,基于多组低对比度车位图像数据,讨论了多种低对比度增强算法的适用性,针对增强算法部分区域产生的车位增强不足与过曝现象导致降低车位线提取的完整性与精度下降的问题,提出峰值直方图均衡化的快速增强算法,结合了PSNR、结构相似性、平均亮度和信息熵等作为客观评价指标,利用了霍夫直线检测统计算法增强结果的车位提取精度,并进行了验证。研究结果表明:本算法能够减少环境信息干扰,保留更多纹理细节,提升全局图像亮度与对比度,其在低照度环境下仍然具有出色的鲁棒性。本算法车位线提取精度超过90%,算法运行时间仅为37.18 ms,能够为低对比度场景下的自动泊车系统提供方法指导。