基于自动策略的脑图像分割

目的脑图像分割在外科手术规划和脑疾病诊断等方面都起着极为重要的作用,建立脑图像分割的自动策略成为一种需要。方法通过各向异性滤波,统计阈值分割,数学形态学滤波,和基于模糊连接算法对脑图像进行自动分割。结果实验表明这种分割策略能取得良好的分割结果。结论本文提出的算法可以有效地完成脑图像的自动分割工作。     

脊椎图像分割和配准的研究进展

脊柱结构非常复杂,位置极其重要,脊柱手术不仅手术风险性大,而且对医生熟练程度要求高。利用图像导航系统进行脊柱外科手术能够降低风险、减少术后并发症,而脊柱图像分割和配准是其中的关键步骤。本文归纳总结脊柱图像分割和配准方法并对其进行分类,并在分析现行方法基础上指出这一领域存在的不足,并展望未来的发展。     

子宫肌瘤图像分割研究进展

子宫及子宫肌瘤分割是治疗肌瘤的关键,目前大部分分割工作还是由医生手动完成,为了提升医生工作效率,提高分割精度,越来越多人开始研究自动分割技术以减轻医生工作量。为了探究子宫及子宫肌瘤自动分割的研究现状,本文综述了近年来各种子宫及子宫肌瘤图像分割的方法,包括聚类、水平集等一些传统分割方法,也囊括了最新的深度学习分割方法,文章的最后对子宫及子宫肌瘤自动分割前景进行了展望。     

海马结构的磁共振图像分割方法

1 海马结构MR图像分割的目的和意义 图像分割(image segmentation)是指根据区域的相似性以及区域间的不同,将一幅图像分割成若干互不交迭区域的过程.海马结构的图像分割就是在图像上把海马结构的边界找出来,使其成为一个连通、闭合区域的过程.海马结构体积测量(volume measurement)在颞叶癫痫、老年性痴呆、遗忘综合征、精神分裂症等神经系统疾病的临床诊断、治疗、疗效评价及计算机辅助诊断(CAD)等方面有重要的应用价值[1].海马结构(hippocampal formation)的图像分割是海马结构体积测量、三维重建的关键和基础.因此,海马结构的图像分割在临床上具有重要意义.临床上使用的分割方法主要还是以人工分割为主,因此研究适用于海马结构MR图像分割的方法有很广的临床应用价值[2,3].     

超声图像分割的研究进展

医学图像主要包括CT、MRI、X线、超声等,其中超声检查价格低,对软组织成像效果好,对人体基本无伤害,目前临床已广泛应用。超声图像分割对后期图像分析有很大作用,可为临床诊断及放疗摆位等提供参考。本文就超声图像分割的传统方法、基于形变模型的分割方法及结合深度学习方法的研究进展进行综述。     

基于MRI图像的主动脉分割与三维建模

目的基于MRI图像序列建立主动脉的三维几何模型并进行计算网格的划分,以用于主动脉血流动力学特性的模拟。方法采用心电R波触发和呼吸控制的方式在体扫描得到心动周期20个时相760幅MRI图像,利用Mimics软件对所获取的图像序列进行图像预处理、分割和三维重建,然后将所建立的三维模型导入到ADINA软件中进行计算网格的划分。结果建立了20个主动脉三维模型,分别代表主动脉在心动周期不同时相的状态,同时,还实现了计算网格的划分。结论该方法可得到进行主动脉血流动力学仿真所需的三维几何模型和计算网格;同时,该方法也可用于人体其他组织的三维建模和网格划分。     

基于数学形态学人脑MR图像感兴趣区域的提取

背景:在人脑MRI图像中感兴趣区域提取中,应用数学形态学方法取得了较好的效果,但是在抗噪性能和结构元素选取时存在一些不足之处,使得提取效果有缺陷.目的:在数学形态学的基础上,采用一系列改进的数学形态学方法,以期清晰完整地提取人脑MR图像中的感兴趣区域如脑脊液部位,为医学诊断提供准确信息.方法:首先采用复合形态学滤波去除脉冲和高斯噪声,用高低帽变换进行图像增强,然后用形态分水岭阈值分割提取脑部各成分,对分割出的脑脊液图像进行形态开闭滤波、边缘跟踪和灰度填充后,运用抗噪型边缘检测算子检测出清晰完整的脑脊液区域边缘,最后在原图像中用彩色标定,突出感兴趣区域.结果与结论:综合应用多种数学形态学算法,清晰完整地提取了人脑MRI图像中的感兴趣区域--脑脊液部位.经验证,该方法具有简单、快速、精度高、适用性强等特点.     

图像分割在医学图像中的研究方法及应用

图像分割是指将一幅图像分解为若干互不交迭区域的集合,是图像处理与机器视觉的基本问题之一.医学图像分割是图像分割的一个重要应用领域,也是一个经典难题.本文从应用的特定角度,对近年来医学图像分割的新方法或改进算法进行综述,并简要讨论了每类分割方法的特点及应用.     

基于双源CT图像的冠状动脉分割

背景：在计算机辅助下,从双源CT图像中把三维冠状动脉分割出来能为其定量评价提供基础。但冠状动脉的三维形态复杂多变,且其管径细小,因而实现冠状动脉的高精度分割是一项有挑战性的课题。目的：解决冠状动脉难以实现高精度分割的问题。方法：采用三步数据处理策略实现冠状动脉分割。先采用阈值方法对三维双源CT图像进行预分割;然后,采用交互式的策略分割出与主动脉相连的左、右冠状动脉始端;最后,根据冠状动脉始端的位置,利用形态学方法和三维断层图像相邻层间的关系分割出三维冠状动脉。结果与结论：提出的基于形态学与断层图像层间关系的分割方法能较精确地从双源CT图像中分割出左、右冠状动脉,说明该方法适用于三维冠状动脉的分割。     

基于配准与ASM方法的医学图像分割

目的随着医学图像数据的急剧增长,建立从医学图像中自动分割特定解剖结构的算法。方法首先,获取的脑图像体数据集通过与参考体数据集的配准,使对应层图像包含与参考数据相似的解剖结构;然后利用训练得到的统计形状模型自动定位、分割指定的解剖结构。结果实验表明这种算法能取得良好的分割结果。结论本文提出的基于互信息的图像配准和统计形状模型的分割算法,能够实现从体数据中自动定位解剖结构所在的图像位置并分割出目标结构。     

基于MeanShift方法的肝脏CT图像的自动分割

目的 探讨基于Mean Shift方法的肝脏CT图像的自动分割算法,以实现肝脏的自动分割。方法 首先对原始图像进行单次Mean Shift平滑 ,滤除噪声的影响以增强算法的鲁棒性,然后通过Mean Shift迭代自动选取初始种子点,最后采用基于区域生长的方法实现肝脏CT图像的自动分割。结果 实验证明此方法是一个准确、快速和有效的肝脏自动分割方法。结论 采用本文中提出的方法,可有效地实现肝脏的自动分割。     

基于期望值最大化方法的磁共振图像人脑组织分割

目的利用期望值最大化方法进行磁共振图像的人脑组织分割。方法在分析当前常用的医学图像分割方法的基础上,提出一种基于统计理论的期望值最大化分割方法,并给出了相应的理论算法模型和实现步骤,最后用Visual C 6.0编程,并对磁共振大脑图像进行实验,并与应用SPM软件对同一幅图像的分割结果进行分析比较。结果本文分割方法与SPM软件的分割结果非常接近,大脑灰质、白质、脑脊液等组织之间边界清晰,总体不确定性较小。结论本文分割方法切实可行,分割效果较好,为进一步的磁共振图像分析和疾病研究提供了一种有效工具。     

Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images

The objective of this study was to investigate the use of speckle statistics as a preprocessing step for segmentation of the myocardium in echocardiographic images. Three-dimensional (3D) and biplane image sequences of the left ventricle of two healthy children and one dog (beagle) were acquired. Pixel-based speckle statistics of manually segmented blood and myocardial regions were investigated by fitting various probability density functions (pdf). The statistics of heart muscle and blood could both be optimally modeled by a K-pdf or Gamma-pdf (Kolmogorov-Smirnov goodness-of-fit test). Scale and shape parameters of both distributions could differentiate between blood and myocardium. Local estimation of these parameters was used to obtain parametric images, where window size was related to speckle size (5 x 2 speckles). Moment-based and maximum-likelihood estimators were used. Scale parameters were still able to differentiate blood from myocardium; however, smoothing of edges of anatomical structures occurred. Estimation of the shape parameter required a larger window size, leading to unacceptable blurring. Using these parameters as an input for segmentation resulted in unreliable segmentation. Adaptive mean squares filtering was then introduced using the moment-based scale parameter (sigma(2)/mu) of the Gamma-pdf to automatically steer the two-dimensional (2D) local filtering process. This method adequately preserved sharpness of the edges. In conclusion, a trade-off between preservation of sharpness of edges and goodness-of-fit when estimating local shape and scale parameters is evident for parametric images. For this reason, adaptive filtering outperforms parametric imaging for the segmentation of echocardiographic images.     

Interactive segmentation based on the live wire for 3D CT chest image analysis

Object The definition of regions of interest (ROIs) such as suspect cancer nodules or lymph nodes in 3D MDCT chest images is often difficult because of the complexity of the phenomena that give rise to them. Manual slice tracing has been used commonly for such problems, but it is extremely time consuming, subject to operator biases, and does not enable reproducible results. Proposed automated 3D image-segmentation methods are generally application dependent, and even the most robust methods have difficulty in defining complex ROIs. Materials and methods The semi-automatic interactive paradigm known as live wire has been proposed by researchers, whereby the human operator interactively defines an ROI’s boundary, guided by an active automated method. We propose 2D and 3D live-wire methods that improve upon previously proposed techniques. The 2D method gives improved robustness and incorporates a search region to improve computational efficiency. The 3D method requires the operator to only consider a few 2D slices, with an automated procedure performing the bulk of the analysis. Results For tests run with five human operators on both 2D and 3D ROIs in 3D MDCT chest images, the reproducibility was >97% and the ground-truth correspondence was at least 97%. The 2D live-wire approach was ≥14 times faster than manual slice tracing, while the 3D method was ≥28 times faster than manual slice tracing. Finally, we describe a computer-based tool and its application to 3D MDCT-based planning and follow-on live guidance of bronchoscopy. Conclusion The live-wire methods are efficient, reliable, easy to use, and applicable to a wide range of circumstances.     

基于梯度向量流snake模型的可视人体图像骨组织分割

为克服传统snake模型不能适应结构复杂的解剖图像、初始轮廓必须充分接近物体边缘的缺点,本研究将基于梯度向量流(GVF)的snake模型用于可视人计划(VHP)图像中骨组织的分割,并修改梯度向量流(GVF)模型,使之适用于彩色图像;针对VHP彩色解剖图像数据量巨大的特点,将多尺度思想应用到snake模型中,以提高处理速度.这种方法提高了计算效率,节省了70%分割时间,得到了理想的精确度,对研究解剖结构、组织定量化测定等具有较高的实用意义.     

A 3D generalization of user-steered live-wire segmentation

We have been developing user-steered image segmentation methods for situations which require considerable human assistance in object definition. In the past, we have presented two paradigms, referred to as live-wire and live-lane, for segmenting 2D/3D/4D object boundaries in a slice-by-slice fashion, and demonstrated that live-wire and live-lane are more repeatable, with a statistical significance level of P < 0.03, and are 1.5-2.5 times faster, with a statistical significance level of P < 0.02, than manual tracing. In this paper, we introduce a 3D generalization of the live-wire approach for segmenting 3D/4D object boundaries which further reduces the time spent by the user in segmentation. In a 2D live-wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment is the minimum-cost path between the two points, described as a set of oriented pixel edges. This segment is found via Dijkstra's algorithm as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary is identified as a set of consecutive boundary segments forming a "closed", "connected", "oriented" contour. The strategy of the 3D extension is that, first, users specify contours via live-wiring on a few slices that are orthogonal to the natural slices of the original scene. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to subsequently trace optimum boundary segments automatically in all natural slices of the 3D scene. A 3D object boundary may define multiple 2D boundaries per slice. The points on each 2D boundary form an ordered set such that when the best boundary segment is computed between each pair of consecutive points, a closed, connected, oriented boundary results. The ordered set of points on each 2D boundary is found from the way the users select the orthogonal slices. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live-wire is more repeatable, with a statistical significance level of P < 0.0001, and 2-6 times faster, with a statistical significance level of P < 0.01, than the 2D live-wire method, and 3-15 times faster than manual tracing.