基于能量最小化的肾脏计算断层扫描图像分割方法

随着成像技术的不断发展, 医学图像处理在计算机辅助诊断和病变管理中的重要作用日渐突出, 而计算断层扫描序列图像的肾脏组织分割是其中的关键步骤. 本文结合肾脏序列图像的连续性特征, 提出了一种基于活动轮廓和图割方法的能量最小化分割模型来自动分割肾脏组织. 根据相邻切片图像的形状差异与层间距之间的关系, 计算出序列图像中适合图割优化能量函数的最优范围. 能量函数采用测地活动轮廓模型和Chan-Vese模型的综合形式, 兼顾了目标的边界和区域信息. 随后, 利用图割方法优化离散化的能量函数, 驱使活动轮廓逐渐向目标边界靠近, 直至收敛为止. 对30组腹腔序列图像进行了算法测试, 实验表明基于能量最小化的分割方法能够有效地提取出序列图像中的肾脏组织, 其分割结果的平均Dice系数达到了93.7%. 关键词： 计算层析 肾脏分割 能量最小化 连续性     

基于X射线断层成像的逆向工程中轮廓序列分割算法的原理及实现

为了实现基于X射线断层成像(CT)的逆向工程中具有参数识别的三维图像重构, 提出了一种分割轮廓序列的新算法. 首先通过一定角度的射线法来得到轮廓间的嵌套关系,然后采用扫描一次关系矩阵生成轮廓树的方法实现层内轮廓定位, 最后运用轮廓间定量、定性的属性判定来完成层间的轮廓匹配. 通过实例, 本文提供的算法可以准确、快速地分割CT零件中的轮廓序列.     

基于3D全卷积神经网络的肺叶自动分割方法

基于电子计算机断层扫描(CT)影像的肺叶分割是医生诊断和治疗肺部疾病的重要参考之一,但肺叶边界的模糊以及手动分割的巨大工作量使得医生难以准确、快速地分割肺叶。为此,提出了一种基于新型3D全卷积神经网络的肺叶自动分割方法。对原始CT图像进行预处理,然后利用预处理后图像训练卷积神经网络,再将待分割图像输入到训练好的网络模型中,实现CT图像中肺叶的自动分割。实验数据包括来自上海市肺科医院的50例肺部疾病患者的CT图像,30例被用于训练,20例被用于测试。对分割结果进行了定量评价,其中Dice系数为0.961,Jaccard相似系数为0.916。实验结果表明该肺叶自动分割算法具有更好的分割性能以及更强的泛化能力,即使在训练集数据较少的情况下,也能够准确、快速的分割肺叶。     

石油岩芯CT图像裂缝分割算法研究

针对裂缝区域分割的需求和石油岩芯CT图像的特点, 改进了现有的水平集分割算法。首先对图像中值滤波去噪后运用C-V模型对图像进行初分割, 把背景区域和岩芯区域准确分开, 得到岩芯区域的轮廓;然后调整轮廓外区域的灰度值, 使之等于岩芯区域平均灰度值, 增强目标区域;最后再进行RSF模型细分割, 得到最终分割结果。对于高斯噪声污染严重的岩芯图像, 先采用了邻域加窗的非局部均值去噪方法, 再用改进水平集算法分割, 实验结果表明该分割方法是有效的。     

基于局部直方图的目标分割方法

图像分割是图像精确跟踪中的重要组成部分 ,是后续进行图像识别的基础。在介绍传统阈值分割方法的基础上 ,提出了一种应用序列图像跟踪、利用跟踪区域的局部直方图来计算分割阈值的方法。该方法利用相邻两帧图像数据的相关性 ,根据跟踪区域的灰度信息自动调节每帧图像的分割阈值 ,使在跟踪区域内的目标得到了较好的分割效果。运用该算法 ,不仅取得了良好的分割效果 ,而且结果证明该算法具有较强的自适应性     

基于聚类分割和形态学的可见光与SAR图像配准

针对可见光与SAR图像灰度差异大,共有特征提取难的问题,提出了一种基于k-均值聚类分割和形态学处理的轮廓特征配准方法。利用k-均值聚类算法对两类图像进行分割,得到图像分割区域;通过形态学处理,有效减少SAR图像斑点噪声影响,准确提取两类图像的封闭轮廓;采用轮廓不变矩理论,引入矩变量距离均值、方差约束机制和一致性检查的匹配策略,获取最佳匹配对,实现了两类图像的配准。通过实验,三组图像的配准精度分别达到0.3450、0.2163和0.1810,结果表明该法可行且能达到亚像素的配准精度。     

传统解剖学特征与深度学习相结合的肺叶分割算法

CT图像中肺叶位置的确定对于肺部疾病的准确定位以及定性定量分析具有重要意义。为了提高肺叶自动分割准确率,提出了一种结合气管,血管等传统解剖学特征以及深度学习的肺叶分割算法。对原始图像进行预处理,获取肺实质、气管、血管以及基于深度学习网络的肺裂分割结果;整合来自多个解剖结构的信息生成分水岭分割所需成本图像;通过基于深度学习网络的肺叶粗分割结果,获取肺叶标记区域;执行基于标记的分水岭分割,实现肺叶的自动分割。选取了来自上海市肺科医院的20例含有肺部疾病患者的CT图像对该方法进行验证,最终的Jaccard相似性系数为92.4%。实验结果表明方法具有较高的肺叶分割精度,并且具有较强的鲁棒性。     

基于水平集的牙齿牙槽骨图像分割

口腔锥形束计算机断层扫描（Cone Beam Computed Tomography,CBCT）图像中牙齿及牙槽骨的分割对骨性结构的三维重建提供了基础,是实现牙齿牙槽骨三维可视化的必要步骤.本文根据牙齿及牙槽骨特点,将一种改进的势阱函数与水平集模型结合,克服以往势阱函数在部分区域出现“停止演化”或“过快演化”的缺陷,并将其应用在对牙齿牙槽骨的分割当中.采用多次小方差高斯滤波叠加的方式对图像进行序贯滤波预处理,解决单一方差高斯滤波难以有效滤除CBCT图像中噪声的问题,为准确分割提供了条件;基于序列图像相邻两张图片中同一牙齿的轮廓变化不大这一特点,以当前层的分割结果作为下一层曲线演化的初始轮廓,使得用更少的迭代次数得到相同结果,从而提高分割速度.另外,本文还将该算法应用于口腔磁共振图像中,并成功对单颗牙齿进行了分割.     

基于PCA的彩色印刷图像色彩分割算法

针对彩色图像的印刷过程中,原图像的色彩分割问题,提出了基于PCA（主成分分析）并结合其它典型彩色图像分割方法的新分割算法。该算法首先利用PCA算法把图像分解为主特征分量和残特征分量两分量图;然后采用二次分水岭算法对残特征分量图进行分割;利用K-Means算法对主特征分量图进行聚类初分割,接着对聚类初分割后的图像进行相似色彩区域融合;最后把分割后的两分量图的进行融合,得到最终的分割结果图。该算法可以应用于彩色印刷图像的色彩自动分割和彩色印刷过程的自动色彩控制中。     

主动形状模型分割方法对光学重建的影响

在非匀质成像中,器官形状是影响建模光在生物体内传播过程的重要因素,它能直接影响荧光分子断层成像(FMT)的重建过程。器官图像的手动分割过程较为复杂,且对图像质量要求较高,而边缘检测、区域生长、主动轮廓模型等自动分割方法在处理复杂医学图像时存在很大的局限性。因此,使用基于主动形状模型(ASM)的自动分割方法,对小鼠器官图像进行准确分割,并使用基于L1范数优化的重建算法实现光源重建。为分析基于ASM的器官图像分割精度与重建精度的关系,采集小鼠计算机断层扫描(CT)数据并进行真实实验,与流行的基于Snake模型的分割算法进行比较。实验结果表明,ASM算法可以替代手动分割,不影响光源的位置重建。     

Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning

The purpose of this study was to design the steps necessary to create a tumor volume outline from the results of two automated multispectral magnetic resonance imaging segmentation methods and integrate these contours into radiation therapy treatment planning. Algorithms were developed to create a closed, smooth contour that encompassed the tumor pixels resulting from two automated segmentation methods: k-nearest neighbors and knowledge guided. These included an automatic three-dimensional (3D) expansion of the results to compensate for their undersegmentation and match the extended contouring technique used in practice by radiation oncologists. Each resulting radiation treatment plan generated from the automated segmentation and from the outlining by two radiation oncologists for 11 brain tumor patients was compared against the volume and treatment plan from an expert radiation oncologist who served as the control. As part of this analysis, a quantitative and qualitative evaluation mechanism was developed to aid in this comparison. It was found that the expert physician reference volume was irradiated within the same level of conformity when using the plans generated from the contours of the segmentation methods. In addition, any uncertainty in the identification of the actual gross tumor volume by the segmentation methods, as identified by previous research into this area, had small effects when used to generate 3D radiation therapy treatment planning due to the averaging process in the generation of margins used in defining a planning target volume.     

Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.     

Edge linking and image segmentation by combining optical and digital methods

We present a hybrid method for segmentation of intensity images, which combines an optical contouring technique and digital algorithms for linking edge points or image segmentation. In a first stage, the digital image to be processed is displayed in a twisted-nematic liquid-crystal display (LCD), which is placed between a polarizer–analyzer pair at 45 deg (instead of 90 deg as occurs in standard LCDs). It is not difficult to demonstrate that the proposed setup produces a resultant image with very pronounced dark contours at middle intensity. After the optical preprocessing, two different digital algorithms are applied: an edge linking algorithm (modified chain code) and a simple thresholding technique for image segmentation. The proposed procedure works well with monochromatic and color images. The method could be useful as a robust technique for segmentation of large images in real-time, which presents potential applications in medical and biological imaging.     

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.     

Snake-based method for the segmentation of objects in multichannel images degraded by speckle

We recently proposed a new approach for the segmentation of speckled images based on active contours (snakes) [e.g., Opt. Commun. 137, 382 (1997)]. We propose an extension of this approach to multichannel data. Two solutions are compared based on hypotheses on the possible mean intensity variation between the channels. Each solution is optimal for a certain class of input images, but one solution shows better or equivalent performance for both input image classes. This result opens new perspectives for the segmentation of multichannel images with the snake-based approach.     

A data augmentation approach to train fully convolutional networks for left ventricle segmentation

Left ventricle (LV) segmentation plays an important role in the diagnosis of cardiovascular diseases. The cardiac contractile function can be quantified by measuring the segmentation results of LVs. Fully convolutional networks (FCNs) have been proven to be able to segment images. However, a large number of annotated images are required to train the network to avoid overfitting, which is a challenge for LV segmentation owing to the limited small number of available training samples. In this paper, we analyze the influence of augmenting training samples used in an FCN for LV segmentation, and propose a data augmentation approach based on shape models to train the FCN from a few samples. We show that the balanced training samples affect the performance of FCNs greatly. Experiments on four public datasets demonstrate that the FCN trained by our augmented data outperforms most existing automated segmentation methods with respect to several commonly used evaluation measures.     

Reduced field-of-view DTI segmentation of cervical spine tissue

The number of diffusion tensor imaging (DTI) studies regarding the human spine has considerably increased and it is challenging because of the spine’s small size and artifacts associated with the most commonly used clinical imaging method. A novel segmentation method based on the reduced field-of-view (rFOV) DTI dataset is presented in cervical spinal canal cerebrospinal fluid, spinal cord grey matter and white matter classification in both healthy volunteers and patients with neuromyelitis optica (NMO) and multiple sclerosis (MS). Due to each channel based on high resolution rFOV DTI images providing complementary information on spinal tissue segmentation, we want to choose a different contribution map from multiple channel images. Via principal component analysis (PCA) and a hybrid diffusion filter with a continuous switch applied on fourteen channel features, eigen maps can be obtained and used for tissue segmentation based on the Bayesian discrimination method. Relative to segmentation by a pair of expert readers, all of the automated segmentation results in the experiment fall in the good segmentation area and performed well, giving an average segmentation accuracy of about 0.852 for cervical spinal cord grey matter in terms of volume overlap. Furthermore, this has important applications in defining more accurate human spinal cord tissue maps when fusing structural data with diffusion data. rFOV DTI and the proposed automatic segmentation outperform traditional manual segmentation methods in classifying MR cervical spinal images and might be potentially helpful for detecting cervical spine diseases in NMO and MS.     

图像分割中采用自适应三次B样条修饰轮廓线

大多数由像素灰度值或灰度相关参数获得图像轮廓线的方法由于受到图像噪声、量化误差以及灰阶的梯度分布等方面的影响 ,获得的边缘轮廓线是不光滑的 ,带有小而密集的不规则锯齿或毛刺 ,这不仅不符合实际情况 ,而且会给进一步的图像处理带来困难。为了获得连续光滑的轮廓线 ,提出了一种方法 :先以动态规划算法提取全局最优的轮廓线 ,然后用一种自适应三次B样条对获得的轮廓线进行修饰和平滑处理。该样条可根据轮廓线不同处的曲率变化情况 ,自适应地调整控制点的分布。在各类图像上的试验表明 ,该方法即有效的消除了轮廓线上的小锯齿 ,又保留了轮廓线的特征细节     

Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology

Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.     

Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming

Segmentation of the left ventricle from cardiac magnetic resonance images (MRI) is very important to quantitatively analyze global and regional cardiac function. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic left ventricle segmentation on short-axis cardiac MRI. The database used in this study consists of three data sets obtained from the Sunnybrook Health Sciences Centre. Each data set contains 15 cases (4 ischemic heart failures, 4 non-ischemic heart failures, 4 left ventricle (LV) hypertrophies and 3 normal cases). Three key techniques are developed in this segmentation algorithm: (1) ray scanning approach is designed for segmentation of images with left ventricular outflow tract (LVOT), (2) a region restricted technique is employed for epicardial contour extraction, and (3) an edge map with non-maxima gradient suppression approach is put forward to improve the dynamic programming to derive the epicardial boundary. The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2 mm. The overlapping dice metric is about 0.92. The regression and determination coefficient between the experts and our proposed method on the ejection fraction (EF) is 1.01 and 0.9375, respectively; they are 0.9 and 0.8245 for LV mass. The proposed segmentation method shows the better performance and is very promising in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.