脑梗塞患者MR图像分割的研究

目的研究一种可实现脑梗塞患者MR图像脑组织分割的算法.方法根据脑梗塞患者MR图像中脑组织的区域和边缘的特性,对传统水平集算法进行改进,实现了对特定目标体分割的能力,降低了边界泄漏发生的可能性.结果通过体膜和大量脑梗塞患者MR图像实验和SPM5对比,实验证实了改进算法对MR图像分割的准确性和鲁棒性. 结论该算法为脑梗塞患者的脑图像分析和脑组织测量提供了一种有效的分割方法.     

一种脑MRA图像自动分割算法及其在图像导引神经外科中的应用

图像导引神经外科技术是在神经外科手术过程中减小患者刨伤的有效方法。在这种方法中，医学图像分割质量的好坏直接影响着手术过程的准确性。本研究提出了一种新的基于MRA图像的自动分割算法，这种算法通过各向异性滤波，统计阈值分割，数学形态学滤波，和基于边界距离场的活动轮廓模型来对MRA图像进行自动分割和分割结果的可视化。数据实验表明用这种算法对MRA图像进行分割的结果可以有效地用于图像导引神经外科，并且算法具有一定的鲁棒性。     

基于交叉熵和GVF-Snake的子宫肌瘤高强度聚焦超声图像自动分割算法

目的:高强度聚焦超声(HIFU)广泛应用于肿瘤无创治疗,目前手术中多用超声成像技术进行导航,但由于HIFU图像对比度低,信噪比低以及目标边界模糊等缺点,HIFU图像的目标识别与分割是重点也是难点,所以需要提出能够自动快速获得HIFU图像肿瘤轮廓的分割方法。方法:GVF-Snake模型算法能够有效地利用超声图像的局部与整体信息实现边界的准确定位,非常适用于HIFU图像分割,但是作为参数活动轮廓模型,GVF-Snake对初始轮廓的依赖性较强,而且通常采用手画初始轮廓,增加了人为因素对试验结果的干预。针对GVF-Snake的相关特性,本文提出用二维最小交叉熵阈值分割法来提取初始轮廓。交叉熵是度量两个统计概率分布之间信息量差异的物理量,分别表征分割前后图像中像素特征向量的概率分布,当原始图像和分割图像之间的信息量差异最小时,便得到最优阈值。使用二维最小交叉熵算法求得初始轮廓后,进而使用GVF-Snake模型收敛,得到最终结果。结果:该算法对HIFU图像中子宫肌瘤的识别与分割具有较为理想的效果,统计结果显示灵敏度平均值达到87.56%,标准化的Hausdorff距离指数平均值达到4.95%,整体算法的运行时间平均值达到2.16 s。结论:该分割算法通过GVF-Snake自动生成初始轮廓,避免了人为干预,整体分割算法快速精准,取得了较好的实验结果,为其在HIFU设备的应用奠定了基础。     

基于边界跟踪算法的脑磁共振图像分割

目的：脑磁共振图像的自动分割是近几年研究的一大热点,本文在通过分析比较当前各种图像分割算法后,介绍了一种基于边界跟踪的脑磁共振图像（MRI）分割算法,在MRI中提取出脑组织部分。方法：应用迭代法对脑磁共振图像进行二值化处理;扫描二值化图像,根据脑组织的形态,确定一点作为脑组织边界的起点;根据边界点的像素特征,对传统的边界跟踪算法进行改进,计算出MRI脑组织的边界,最后应用区域生长法在原始MRI中提取脑组织图像,实现MRI分割。结果：实验结果表明,改进后的边界跟踪算法在提取脑组织边界时,细节处理能力强,不易陷入死循环,具有较高的运算速度。提取的真实脑磁共振图像的脑组织区域,能满足临床的实际需要。结论：对图像二值化处理,对图像有微弱的损害,但是极大地提高了计算速度。与传统的边界跟踪算法相比,改进后的边界跟踪算法分割效率高,更易实现MRI的自动分割。获得的边界曲线在细节上更接近于脑组织的实际边界。     

一种基于水平集的前列腺超声图像自动分割算法

由于超声图像存在着斑点噪声和较低的信噪比,使得传统的分割算法很难应用于超声图像的自动分割,而手工分割方法非常耗时且重复性差.因此提出了一种基于水平集和改进径向浅浮槽算法(RBR,radical bas-relief)的前列腺直肠超声(TRUS)图像全自动分割算法.首先使用Sticks滤波器来去除斑点噪声,并且增强图像的对比度.然后使用径向浅浮槽算法来对图像进一步增强,通过使用形态学算法和边界填充得到前列腺的初始轮廓,使用该轮廓来初始化水平集算法,从而实现全自动分割.实验结果表明,该方法能有效地去除斑点噪声的影响,并且能够对前列腺超声图像实现较好的分割效果.     

基于单水平集函数的细胞分割算法

目的：由于细胞图像十分复杂,传统的基于像素或者边界的图像分割方法难以精确的实现细胞分割。因此,需要设计一种可以实现细胞图像精确分割的方法。方法：结合大津分割算法和主动轮廓模型的优点,设计出一种基于单水平集函数的细胞分割算法,首先对细胞图像大津分割,其结果作为水平集函数的初始值,然后使用迭代法对水平集函数演化。采用MATLAB对显微镜下获取的细胞图像进行试验,将本文改进后的算法与常规的算法进行了对比。结果：与传统的水平集分割算法相比,本文方法对细胞图像分割结果更加准确,迭代次数减少一半左右,因此分割时间也减少了一半左右。结论：结合细胞图像的结构特点,利用大津分割结果作为主动轮廓模型的初始值,可有效解决主动轮廓模型因为初始值设置不当导致的分割缺陷问题,水平集函数能够跟踪拓扑结构变化,具有计算精度高、算法稳定、优化边界清晰光滑等优点,在本文中得到了充分的应用。因此本文所提出的算法能够高效地实现细胞图像的分割。     

基于贝叶斯分类的水平集MR图像分割方法

本文介绍一种结合贝叶斯分类的水平集方法用于人体MR图像分割.本方法首先通过贝叶斯分类模型计算出水平集曲线位于边界上的概率;其次,将与此概率相关联的区域决策影响因子添加在水平集函数方程中;最终,实现利用图像的区域信息提高水平集曲线识别边界能力的目的.分割实验结果表明,该方法较好地克服了传统水平集方法在MR图像分割中存在的边界泄露问题.与其他MR图像分割方法比较,本方法具有更优的分割结果.     

改进的Brain Extraction Tool算法及其在脑实质分割中的应用

BET(Brain Extraction Tool)算法是一种常用的从磁共振(MRI)脑图像中分割脑实质的工具,在实际应用中发现,BET算法对正常脑实质的分割精度较高,但对有病灶的脑实质分割精度较差。根据BET算法存在的问题,改进原BET算法中不合理的u_3,简化了计算繁琐的u_2,并将其应用于分割MRI图像中的脑实质。首先:选择序列图像中间层,对其应用两次改进后的BET算法获得精确分割结果;然后:将获得的边界向其中心缩小一定比例后作为与其相邻层的初始边界再次应用修改后的算法获得该层精确边界;最后,不断重复上述步骤直至所有层分割结束。改进后的算法对脑部图像分割结果与人工分割结果的重叠率达到92.92%,而使用FSL中提供的BET工具的分割结果与人工分割结果的重叠率为88.94%。改进后的算法相比原BET算法能够更加准确地分割MRI图像中的脑实质。     

基于活动轮廓模型的B超乳腺肿瘤图像分割

乳腺良、恶性肿瘤在形态上存在较大差异,其形态学变化可以作为临床医生判断的重要依据.目前临床广泛使用的超声成像系统采用的是基于阈值的分割方法 或医生手动的分割方法,这对大多数灰度差异不显著的B超图像分割效果不好.本文采用基于形变模型的图像分割算法来分割肿瘤区域,并对自动snake算法做了一些改进,实验结果 表明该算法适用性好,分割结果 接近自然边界,且对B超图像固有的斑点噪声不是很敏感,较好实现了乳腺肿瘤区域的交互式半自动提取.     

基于T-Snake模型的超声左心室心肌分割方法的研究

提出一种基于T-Snake模型的超声图像中左心室心肌分割的新方法。首先,将自适应加权中值算法引入基于窗口的各向异性扩散滤波方法,有效滤除超声图像中的斑点噪声;其次,根据心脏短轴截面心肌近似成圆形这一先验知识,将图像从笛卡尔坐标转换到极坐标,提高后续分割方法的鲁棒性;最后,在传统T-Snake模型基础上,增加具有自适应特性的膨胀力和区域能量,可有效提取弱边界和不连续的边界,实现左心室心肌内外轮廓的自动分割。对模拟图像和临床数据进行的实验证明,自动分割结果的平均相对差异度小于5%,平均相对重叠度高于90%,实验结果验证了本方法的可行性。     

Correction of lung boundary using the gradient and intensity distribution

We propose a new method for correcting the segmented lung boundary in expiratory and inspiratory CT. First, the initial lung boundary is extracted by using density-based segmentation. Second, the scope for the boundary propagation is computed by generating and analyzing the gradient profiles with an adaptive length. The definition of the scope helps to prevent the leakage outside the scope and improves the efficiency of the propagation. Finally, the boundary is propagated within the defined scope using a speed function. The speed function is based on the gradient and intensity distribution and prevents the boundary from converging to the local gradient maxima. The results of the lung boundary correction are evaluated by visual inspections, accuracy evaluations and processing time. Experimental results show that the proposed method corrects the lung boundary reliably and reproducibly.     

一种简化的袖带测量模型及在收缩压测量中的应用研究

传统的波动法血压无创测量技术由于利用了统计学原理，因此必须造成测量时的个体误差。作者对袖带测量的物理进行了研究，根据已有的结论，提出了一种袖带测量过程的物理模型，根据这一模型提出了一种人体血压的无创测量方法，该方法不象传统方法那样采用统计学原理，从而避免了所有个体采用统一的血压判据而导致的个体测量误差，实际实验表明，该技术的测量准确性要优于传统的波动法测量技术，这对改善医学临床血压无创自动测量技术     

A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms

A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics).     

基于CT的头颅骨三维表面重建

基于头颅骨的计算机断层 (CT)图像 ,研究了一种对头颅骨表面三角化重建的方法。首先通过交互式分割选择出感兴趣区域 (ROI) ,提取出ROI的轮廓点并根据位置关系对轮廓点排序。然后基于局部形态最佳的思想 ,对两相邻断层图像上的轮廓点进行三角化 ,从而得到具有最佳几何形态的三角化表面。最后用实际的CT图像对所提出的算法进行实验 ,验证了此种方法的快速、有效和鲁棒性。     

Extraction of Metastatic Lymph Nodes from MR Images Using Two Deformable Model-based Approaches

We presented and evaluated two deformable model-based approaches, region plus contour deformation (RPCD), and level sets to extract metastatic cervical nodal lesions from pretreatment T2-weighted magnetic resonance images. The RPCD method first uses a region deformation to achieve a rough boundary of the target node from a manually drawn initial contour, based on signal statistics. After that, an active contour deformation is employed to drive the rough boundary to the real node-normal tissue interface. Differently, the level sets move a manually drawn initial contour toward the desired nodal boundary under the control of the evolvement speed function, which is influenced by image gradient force. The two methods were tested by extracting 33 metastatic cervical nodes from 18 nasopharyngeal carcinoma patients. Experiments on a basis of pixel matching to reference standard showed that RPCD and level sets achieved averaged percentage matching at 82-84% and 87-88%, respectively. In addition, both methods had significantly lower interoperator variances than the manual tracing method. It was suggested these two methods could be useful tools for the evaluation of metastatic nodal volume as an indicator of classification and treatment response, or be alternatives for the delineation of metastatic nodal lesions in radiation treatment planning.     

基于神经网络的人中耳内边界参数识别

目的 研究识别中耳内边界的方法。方法 利用CT获取耳部结构临床资料建立数值模型;利用Matlab建立神经网络识别程序。结果 利用数值模型,在鼓膜外侧施加105dB声压,进行谐响应分析获取训练样本;利用训练成熟的网络识别了中耳内边界参数。结论 数值识别结果表明,所建的中耳内边界参数识别方 法具有良好的识别精度和较快的收敛速度。     

基于主成分分析和重叠直方图统计的视频信号心率测量

光电容积脉搏波描记法(PPG)原理指出,特定波长的光强度在血液容积作用下呈现周期性变化,因此人面部的肤色会随着脉搏的波动发生微小的变化。根据该原理,可以实现低成本、无接触、无电极的舒适的心率测量,对于远程医疗监控等领域有很大的意义。使用普通摄像头采集人脸视频,通过主成分分析(PCA)对人脸的3个区域的绿色分量的均值提取主成分信号,消除部分外界噪声的干扰;提取的主成分信号经平滑处理后,进行去趋势化处理,然后经过所提出的波峰检测方法,提取其波峰,减少波峰的漏检或多检;此外,采用重叠直方图统计的方法,计算心率信号的波峰间隔。在实验中,采用康泰CMS-50D指夹式脉搏血氧仪(测量样本数174个)和美国BIOPAC公司MP150多导生理记录仪记录的ECG(样本数110个),分别与所提取结果通过Bland- Altman分析方法进行对比。该方法与指夹式脉搏血氧仪测量结果的对比实验表明:叠加直方图统计方法的引入,使一致性评估评估的标准差由12.4 次/min减小到8.0 次/min左右,提高约1/3;与ECG的一致性评估的结果均值为1.8次/min,标准差为3.2 次/min,两者均较小,说明两者结果具有很好的一致性。     

Robust non-homomorphic approach for speckle reduction in medical ultrasound images

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.     

Adaptive approximation of the boundary surface of a neuron in confocal microscopy volumetric images

In biomedical visualisation, the isosurface is usually used to represent (approximate) the boundary surface of the structure within biomedical volumetric images. However, in many confocal microscopy volumetric images of neurons, the grey values of the object and/or background are usually uneven. Therefore a fixed isosurface is not suitable for use in approximating the boundary surface of the neuron. A method is proposed to construct the adaptively approximating surface of the boundary surface of the neuron. In this method, the boundary surface of the neuron could be locally and adaptively approximated with different surface patches in different local regions. Consequently, the approximation accuracy has been considerably improved.     

Inferring the impact of linguistic boundaries on population differentiation: application to the Afro-Asiatic-Indo-European case

We present here a quantitative way to assess the impact of language-family boundaries on population differentiation and to evaluate the homogeneity of the genetic processes along these boundaries. Our estimator (delta a) of the impact of the boundary is based on an isolation by distance (IBD) model and measures the added genetic distance between populations located on different sides of the boundary. We compare this statistic with another estimator of group differentiation (F(CT)) computed under an analysis of variance framework that does not assume any particular spatial structure of the populations. Monte Carlo simulations are used to study the behaviour of these statistics under a two-dimensional stepping-stone model. Simulations show that F(CT) can suggest the existence of a frontier when populations only differ because of IBD. This spurious behaviour is much less frequent for the delta a statistic. However, the large variance associated with the delta a statistic, and the fact that it should only be computed in the presence of IBD, may limit the use of this statistic. Overall, the origin and the effect of the boundary is best understood by comparing different statistics and by testing for the presence of IBD on each side of the boundary as well as across the boundary. We illustrate our approach by examining the boundary between Afro-Asiatic and Indo-European populations. These populations are globally genetically differentiated, but the effect of the linguistic boundary on gene flow seems geographically very heterogeneous. This boundary appears to be the result of a secondary contact between two differentiation centres rather than an enhancer of population differentiation.