A novel active contour model for medical image segmentation

A novel segmentation method for medical image with intensity inhomogeneity is introduced. In the proposed active contour model, both region and gradient information are taken into consideration. The former, i.e., region-based fitting energy, draws upon the region information and guarantees the accurate extraction of inhomogeneous image’s local information. The latter, i.e., an edge indicator, weights the length penalizing term to consider the gradient constrain. Moreover, signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function. Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.     

An Improved Singularity Computing Algorithm Based on Wavelet Transform Modulus Maxima Method

IntroductionA recent finding is that noise signal may con-tain hidden information.Such information promis-es to be of application value(forecasting suddencardiac death in patients,or analyzing financialmarkets fluctuation,or predicting the properties ofelectric devices).We must use some approachesfor extracting such hidden information from noise.Conventional approaches include analysis ofmeans,standard deviations and other features ofhistograms,along with classical power spectrumanalysis.Thos…     

基于图像分割的图像检索新方法

利用信息熵理论,提出了一种新的相似性图像检索方法.首先,分割图像,抽取图像的分块颜色信息;再计算图像之间的颜色互信息来确定它们的相似度.与其他的颜色特征描述方法相比,该方法该算法具有抗噪声能力强、精确度高等优点.     

A Target-Orientated Marker Image Binarization Method for Orthopaedic Surgical Navigation System

Camera calibration is the key technique in a C-arm based orthopaedic surgical navigation system. The extraction of marker location information is a necessary step in the calibration process. Ideal marker images should possess uniform background and contain marker shadow only, but in fact marker images always possess nonuniform background and are contaminated by noise and unwanted anatomic information, making the extraction very difficult. A target-orientated marker shadow extraction method was proposed. With this method a proper threshold for marker image binarization can be determined.     

多视点标定图像的交替迭代度量重建方法

提出了一种针对相机内部参数已标定的多视点图像交替迭代度量重建方法.该方法以目标空间中重建的3D点到匹配特征点的反投影线间的距离作为重建误差的测度,使误差函数形式上更简洁且具有明确的几何意义.重建过程通过对相机外部参数中的旋转矩阵、平移量和3D重建点交替迭代完成,其中平移量和3D点一起估计,避免了它们与旋转矩阵中的元素间的尺度差异引起的数值偏差.利用合成数据和真实图像的计算分析均表明本文方法有较高的重建精度,具有实用性.     

Multi-band Image Registration Method Based on Fourier Transform

This paper presented a registration method based on Fourier transform for multi-band images which is involved in translation and small rotation. Although different band images differ a lot in the intensity and features,they contain certain common information which we can exploit. A model was given that the multi-band images have linear correlations under the least-square sense. It is proved that the coefficients have no effect on the registration progress if two images have linear correlations. Finally, the steps of the registration method were proposed. The experiments show that the model is reasonable and the results are satisfying.     

基于eCognition的面向对象农村公路提取

采用面向对象思想,使用高分辨率CBERS 02B HR全色影像对农村公路进行提取。在对影像进行分割后,综合利用影像对象的光谱信息和几何信息,构建道路提取知识库,并使用道路生长法对提取结果进行完善,增加了道路的完整性。与面向像元的方法对比,该方法能取得较好的精度。     

MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM

Imagesegmentationplaysanimportantrolein imageanalysisaswellasinhigh levelimageinter pretationandunderstandingsuchasrobotvision, objectrecognition,andmedicalimaging.Numerous segmentationmethodshavebeendeveloped.How ever,medicalimagesareoftencorruptedbyno…     

PULMONARY VESSEL EXTRACTION BASED ON COHERENCE DIFFUSION AND FAST MARCHING

Clinicalresearchshowsthattheanalysisofpul monaryvesselshelpidentifyingearlyfeaturesofre piratorypathologicchanges.Pulmonaryvesselex ractionplaysanimportantroleinmedicalimage processingandanalysis.Moreover,theresultsof vesselsegmentationprovideatoolforunde…     

Poisson noise removal based on nonlocal total variation with Euler’s elastica pre-processing

An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.     

奇异值分解算法在数字水印技术中的应用

奇异值分解算法应用于数字水印技术中,把水印信息按位置换,对宿主图像分块并做奇异值分解,在奇异值域做数学变换以重复嵌入按位置换的二值水印信息．实验结果表明,该方法既提高了水印的信息量,同时又使得水印图像具有很强的稳健性,能抵抗旋转和裁剪等多种攻击,水印的保密性与鲁棒性均有显著的提高．     

分层CS-LBP和HOG特征融合的人脸识别研究

针对单独的纹理特征只能提取图像的纹理信息而不能得到图像轮廓边缘信息的问题,在人脸识别的特征提取研究中提出了分层CS-LBP和分层HOG特征的融合方法.对图像分别进行多次CS-LBP和HOG特征的提取,得到分层CS-LBP特征提取图像和分层HOG特征提取图像,对分层的特征提取图像再次提取分层CS-LBP和分层HOG特征,并将两种分层特征进行融合,得到更有效的人脸的纹理及边缘轮廓特征.在ORL和GT人脸库上的实验结果验证了所提出的分层特征融合方法的有效性.     

基于深度学习的公路货车车型识别

为判断公路货车车型，并提升货车车型识别的速度与精度，提出基于深度学习的方法对公路货车及其轮轴进行精细化目标检测；采用道路监控拍摄和网络爬取的方式获得了16 403张公路货车侧方图像，建立了货车侧方图像数据集，并采用Retinex理论和加入限制对比度的自适应直方图均衡化(CLAHE)等视觉增强方法预处理所采集图像中的光照不均图像和夜视图像；通过理论分析和对比试验选取单阶段检测网络YOLOv3作为公路货车车型识别的目标检测网络，并从调整先验框和模型输入大小以及引入注意力机制3个方面优化了检测模型；针对单帧图像可能同时出现多辆货车的情况，采用基于目标位置信息挖掘的算法分析了货车与轮轴的位置信息，提出一种通过轮轴中心点与货车预测框位置信息判定公路货车与轮轴隶属关系的方法。研究结果表明：图像经过预处理可显著增强车辆的特征信息，优化后检测模型的网络性能得到提高，通过对目标位置信息的挖掘与利用可以很好地解决货车车型判定问题；优化后的检测模型实时检测速度可达47帧·s^-1,对公路货车车型的识别综合准确率达到了94.4%。该方法实现了对公路货车车型的无接触、快速和准确识别，为公路货...     

人脸非线性视角流形建模方法

针对线性子空间不足以描述头部视角空间非线性变化等因素影响人脸视角流形的精确建模问题,提出一种新的视角流形建模方法,并从理论上将该方法与经典的流形学习建模方法及概念驱动的视角流形建模方法进行比较,通过基于非线性张量分解的人脸及视角识别实验比较视角流形对识别结果的影响,从而给出视角流形的有效性比较.实验结果表明,本文提出的视角流形建模方法比概念驱动的视角流形和TensorFace中的线性视角系数均有更好的识别效果.     

高等级公路路段交通状态融合识别模型

为了给区域交通管理者提供合理、准确的交通状态信息,提出了一种全新的可用于识别高等级公路路段交通状态的方法。针对路段中检测单元上交通状态决策信息的离散、模糊和时变特征,将路段内相邻检测单元同类交通状态决策信息合并,得到有序交通状态块;基于vague集理论提出了一种新的加权距离公式,并将合并后各交通状态块在路段中的位置和长度作为权重,建立了基于交通状态块决策信息的路段综合交通状态融合识别模型。通过算例验证了该模型的有效性,为识别高等级公路路段交通状态提供了一种新途径。     

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.     

MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

Introduction　　 Automating medical image registration is be-coming an increasingly important goal. It formsthe basis of image processing techniques to com-bine information from the different modalities,which are now frequently acquired for many pa-tients. Such combined representations of patientimages have been shown to assistin the interpreta-tion of the complementary information provided bythe modalities[1,2 ] .　　 A variety of imaging modalities gives insightto different parameters and in…     

层次极限学习机用于高光谱图像预测绝缘子污秽度

高光谱图像具有图谱合一、光谱范围广及分辨率高等优势，能精细化地反映物质微观特性. 为此，引入高光谱成像技术以非接触式预测绝缘子污秽度. 考虑到极限学习机具有学习效率高和泛化能力强等优点，提出基于正则化约束极限学习机的绝缘子污秽度预测（extreme learning machine-insulator pollution degree prediction，ELM-IPDP）模型. 此外，为进一步提升预测性能，引入层次极限学习机从复杂的高光谱图像中学习出有效、抽象、判决性特征表示，继而建立基于层次极限学习机的绝缘子污秽度预测（hierarchical ELM-IPDP，HELM-IPDP）模型. 在不同的训练集与测试集比例和不同隐含层神经元个数的情况下分别进行实验，从实验结果可知:ELM-IPDP模型和HELM-IPDP模型的预测性能基本上随着隐含层神经元个数和训练样本的增加而不断提高；当训练集与测试集比例为9∶1时，ELM-IPDP模型的均方根误差和相关系数分别为0.040 3和0.944 7，而HELM-IPDP模型的均方根误差和相关系数分别提升到0.022 3和0.972 0.      

单幅图像融合参考模型的三维人脸重建

基于单幅图像的三维人脸重建以其数据获取简易而具有广阔的应用前景.本文提出了一种融合参考模型的方法实现基于单幅图像的三维人脸重建,其主要思想是以球面调和函数表示图像的光照模型,从而建立图像的光照信息与深度信息之间的解析关系;进而可基于参考模型的深度信息获得输入图像的近似光照系数,再根据近似所得的光照系数恢复其深度信息.该法不仅可完全自动化地实现,而且因不需要多个人脸模型的信息而优于基于统计模型的三维重建;由实验中对不同库中人脸图像及真实采集的人脸图像进行重建的结果发现,本文提出的重建方法是切实可行的.     

Automatic Airway Deletion in Pulmonary Segmentation

A method of removing the airway from pulmonary segmentation image was proposed. This method firstly segments the image into several separate regions based on the optimum threshold and morphological operator,and then each region is labeled and noted with its mean grayscale. Therefore, most of the non-lung regions can be removed according to the tissue‘s Hounsfield units (HU) and the imaging modality. Fifially, the airway region is recognized and deleted automatically through using the priori information of its HU and size. This proposed method is tested using several clinical images, yielding satisfying results.