基于谱聚类与多信息特征融合的图像分割算法

为了改善传统基于聚类的图像分割算法对噪声敏感以及仅使用单一特征无法精确描述目标特性等问题,提出了一种基于区域的多特征图像分割算法。首先,使用 Meanshift 算法对原图像进行预分割,获得一组区域块;其次,提取每个区域块的颜色特征和纹理特征,使用 FCM 算法分别对每个特征进行聚类,针对每个特征获得一个类标签邻接矩阵;再次,将多个邻接矩阵叠加,形成多特征邻接矩阵;最后,使用 NCUT 算法对叠加邻矩阵进行聚类,获得最终分割图像。实验结果表明,基于区域多特征的分割算法优于对比算法,融合多特征对图像分割可以更准确地识别不同的目标结构,具有更好的分割效果。     

A novel method for detection of hard exudates from fundus images based on SVM and improved FCM

Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM (IFCM) as well as support vector machines (SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65 and a mean positive predictive value of 97.25 . With an image-based criterion, our approach reached a 100 mean sensitivity, 96.43 mean specificity and 98.21 mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.     

High accuracy in automatic detection of atrial fibrillation for Holter monitoring

Atrial fibrillation (AF) has been considered as a growing epidemiological problem in the world, with a substantial impact on morbidity and mortality. Ambulatory electrocardiography (e.g., Holter) monitoring is commonly used for AF diagnosis and therapy and the automated detection of AF is of great significance due to the vast amount of information provided. This study presents a combined method to achieve high accuracy in AF detection. Firstly, we detected the suspected transitions between AF and sinus rhythm using the delta RR interval distribution difference curve, which were then classified by a combination analysis of P wave and RR interval. The MIT-BIH AF database was used for algorithm validation and a high sensitivity and a high specificity (98.2% and 97.5%, respectively) were achieved. Further, we developed a dataset of 24-h paroxysmal AF Holter recordings (n=45) to evaluate the performance in clinical practice, which yielded satisfactory accuracy (sensitivity=96.3%, specificity=96.8%).     

基于Bagging与超限学习机的脑力负荷识别模型

为了提高脑力负荷分类准确率,提出一种将Bagging和极限学习机相结合的集成算法。用极限学习机（ELM）作为底层弱分类器,通过多数投票方式决定最终类别的标签,从而构建最终强分类器。实验结果表明,在脑力负荷识别研究问题上,该集成算法的分类准确率在4个被试数据集上分别达到了96.17%、96.02%、92.50%和93.50%。相较于传统的ELM算法,分类准确率在4个被试数据集上分别提升了1.59%、1.34%、2.86%和1.80%。并且新算法在精确率、灵敏度和特异度等评估标准上均高于传统ELM分类器。     

Automatic segmentation of bladder in CT images

Segmentation of the bladder in computerized tomography （CT） images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First, we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder, which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity, specificity, positive predictive value, negative predictive value, and Hausdorffdistance are 86.5%, 96.3%, 90.5%, 96.5%, and 2.8 pixels, respectively. The results show that the bladder can be accurately segmented.     

Proteiomic patterns for endometrial cancer using SELDI-TOF-MS

Serum samples from endometrial cancer (EC) patients and healthy females were analyzed using surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to discover the potential diagnostic biomarker for detection of EC. A preliminary training set of spectra derived from 40 EC patients and 30 healthy women were used to develop a proteomic model that effectively discriminated cancer patients from healthy women. The training set had a specificity of 100% and sensitivity of 92.5% in the EC detection. A blind test set, including 20 new cancer cases and 10 healthy women, was used to validate the sensitivity and specificity of this multivariate model, which had a corresponding results of 60% in specificity and 75% in sensitivity, respectively. The combination of SELDI-TOF-MS with bioinformatics tools could help find new biomarkers and establish the detection of EC with high sensitivity and specificity.     

Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor

To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade Ⅰ-Ⅳ) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma Ⅰ-Ⅳ), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade Ⅰ-Ⅱ with grade Ⅲ-Ⅳ ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade Ⅰ-Ⅱ Astrocytoma was 86.7%, accuracy ofⅢ-Ⅳ Astrocytoma was 84.6% were obtained when grade Ⅰ-Ⅱ Astrocytoma was compared with grade Ⅲ-Ⅳ ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and glioma from brain benign tumors.     

Assessment of Autism Symptoms From 6 to 18 Months of Age Using the Autism Observation Scale for Infants in a Prospective High-Risk Cohort

The objectives were to characterize behavioral signs of autism spectrum disorder (ASD) in younger siblings of diagnosed children (high-risk; HR) and examine classification features of the Autism Observation Scale for Infants (AOSI). Participants (501 HR and 180 low-risk [LR]) were assessed between 6 and 18 months using the AOSI and at age 3 for ASD diagnoses. Total AOSI scores differentiated HR infants later diagnosed with ASD starting at 12 months. ROC analyses identified 12- and 18-month cutoff scores associated with 0.52 sensitivity and 0.74 specificity and 0.73 sensitivity and 0.65 specificity, respectively. Although classification accuracy does not support use as a standalone screen, the AOSI identifies features associated with ASD starting at 6 months and differentiates HR infants with ASD by 12 months.     

Clinically applicable artificial intelligence algorithm for the diagnosis,evaluation, and monitoring of acute retinal necrosis

The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN). The potential application of artificial intelligence(AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples(R2=0.7444, P0.0001) and therapeutic response of ARN(R2=0.999, P0.0001). Therefore, our AI algorithm has a potential application in the clinical aided diagnosis of ARN, evaluation of ARN severity, and treatment response monitoring.     

B型超声诊断胆囊管结石的价值

目的：旨在探讨B型超声检查对胆囊管结石的诊断价值及临床意义。方法：收集本院1995－2001年经手术治疗胆囊管结石病例共89例，分析其中78例术前经超声诊断为胆囊管结石的声像图特征，结果：胆囊管结石具有胆囊结石的声像图特征外，还具有胆囊肿大，胆囊管扩张，超声检查对胆囊管结石具有良好的显示效果，术前超声对胆囊管结石诊断的准确率为87．6％，结论：超声检查对胆囊管结石的诊断具有较高的特异性和敏感性。且具有重要的临床意义。     

Can training improve marker accuracy at detecting contract cheating? A multi-disciplinary pre-post study

Contract cheating occurs when students outsource assessed work. In this study, we asked experienced markers from four disciplines to detect contract cheating in a set of 20 discipline-specific assignments. We then conducted a training workshop to improve their detection accuracy, and afterwards asked them to detect contract cheating in 20 new assignments. We analysed the data in terms of sensitivity (the rate at which markers spotted contract cheating) and specificity (the rate at which markers spotted real student work). Pre-workshop marker sensitivity was 58% and specificity was 83%. Post-workshop marker sensitivity was 82% and specificity was 87%. The increase in sensitivity was statistically significant, but the increase in specificity was not. These results indicate that markers can often detect contract cheating when asked to do so, and that training may be helpful in improving their accuracy. We suggest that markers’ suspicions may be crucial in addressing contract cheating.     

增加环介导等温扩增灵敏度与特异性的两种实用方法

拟以单核细胞增生李斯特菌的特异基因prfA为研究对象,采用二甲基亚砜（DMSO）以及Touchdown-LAMP增加环介导等温扩增的灵敏度与特异性.结果表明,在环介导等温扩增反应体系中添加7.5％的DMSO并采用Touchdown-LAMP方法,克服了假阳性扩增,灵敏度增加了100倍,在灵敏度与特异性方面优于现有的商业化试剂盒.     

Removing the remaining ridges in fingerprint segmentation

INTRODUCTION Fingerprint identification typically goes through feature extraction and feature matching (Zhu et al., 2005b), with fingerprint segmentation being one of the steps for fingerprint feature extraction. A finger-print image usually has to be segmented to remove regions of no interest before some other steps such as enhancement and minutiae detection so that the image processing will consume less CPU time. A fingerprint image generally consists of different regions: non- ridge …     

一种新颖的数字图像篡改检测方法

检测数字图像复制—粘贴型篡改是目前研究热点之一。多数复制—粘贴篡改检测方案只对刚性平移篡改具有较好的检测精度,无法有效应对更复杂的几何变换和常规信号攻击。为优化数字图像复制—粘贴篡改检测效率和精度,首次提出一种使用极坐标复指数变换（PCET）与一致性敏感哈希（CSH）的高效检测算法。首先,计算滑动窗口 PCET 系数,将其作为不变的图像局部特征|然后,根据这些特征,利用 CSH 算法快速、精确地匹配大量密集分块|最后,使用基于密集线性滤波的后处理算法消除匹配结果中的错误匹配并定位重复区域,得到最终检测结果。根据实验结果可知,该算法不仅检测精度平均提高 12.06%,而且处理时间平均缩短315.64s。因此利用对几何变换和常规信号攻击鲁棒的 PCET 系数刻画图像局部特征,并采用基于图像一致性的快速高精度匹配算法 CSH,可有效优化复制—粘贴篡改检测精度和效率。     

Depth-aided inpainting for disocclusion restoration of multi-view images using depth-image-based rendering

A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.     

卵巢肿瘤的三维和二维彩色血管能量成像的临床价值对比研究

目的比较三维彩色血管能量成像（3D-CPA）和二维彩色血管能量成像（CPA）在鉴别诊断卵巢良恶性肿瘤方面的临床应用价值。方法对53例卵巢肿瘤患者,进行二维彩色血管成像和三维血管立体重建,观察肿物血管的空间分布特点,给出定性诊断,与术后病理结果进行对照分析。结果术后病理证实恶性为32例,良性为21例。CPA的敏感性、特异性、阳性预测值、阴性预测值及符合率分别为93。8％,85-7％,90．9％,90％及90。5％．3D-CPA的敏感性、特异性、阳性预测值、阴性预测值及符合率分别为96．9％,76．2％,86．1％,94.1％及88．7％。3D-CPA和CPA两种检测方法比较差异无统计学意义（X^2=0．44,P〉0．05）。结论在鉴别诊断卵巢肿瘤良恶性方面,3D-CPA的诊断效能与CPA一致。但3D-CPA进一步立体、直观地反映卵巢肿瘤血供,明确地定位病变的位置与范围,对妇科手术方案的制定具有指导意义。     

基于混沌的密钥安全分配

利用混沌效应的特性,提出基于混沌的密钥加密算法.混沌加密算法以非线性动力学中的混沌为理论基础,利用其初值敏感性和迭代不会重复性以产生序列随机数,从而应用于密钥数据加密,再把加密的密钥数据进行RSA加密.通过程序仿真表明,该方案能有效加密密钥数据,加密速度快,效果好,安全性强.     

Diagnostic value of computed tomography (CT) histogram analysis in thyroid benign solitary coarse calcification nodules

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.     

An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer

INTRODUCTION Ovarian cancer is the most lethal gynecologicmalignancy. Poor survival rates are mainly attribut-able to late diagnosis. Most patients at diagnosis haveadvanced stage disease. The 5-year survival rate forlate clinical stage ovarian cancer is only 25%, but forearly stage disease, the survival rate can be as high as90%. CA125, the most widely used biomarker forovarian cancer, does not have a satisfying positivepredictive value. In early stage ovarian cancer,40%–50% pati…     

冠心病与血清脂质和血液流变学的关系探讨

本文对55例冠心病患者和5O例正常人作了甘油三酯、胆固醇、载脂蛋白AI,载脂蛋白B、高密度脂蛋白和高密度脂蛋白亚组分含量的测定,同时还作了全血比粘度、聚集指数、血浆比粘度、红细胞压积等血液流变学指标的测定。冠心病组和正常对照组比较,经统计学处理有显著性差异(P<0.01)。并用灵敏性和特异性两个指数将血清脂质血流变学进行比较。在血清脂质中HDL_2—Ch在病人组改变最为明显,其灵敏性为71％,特异性为92％在血液粘度中以低切粘度改变最为明显,其灵敏性为67％,特异性为88％。HDL_2—Ch可作为冠心病发病首选危险因子。血液粘度对冠心病的辅助诊断有一定参考价值,并可作为冠心病的治疗和预后判断指标,HDL_2—Ch和血液低切粘度结合起来应用,对冠心病的诊断、治疗和预后具有重要指导意义。