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1.
神经元干细胞图像的全自动分割与追踪   总被引:1,自引:1,他引:0  
基于显微细胞图像的全自动分割算法,建立了一种全自动追踪序列图像中的神经元干细胞的系统,序列图像的初始图结合了人机交互,干预分割结果。所有的细胞在追踪过程中将其分成惰性细胞,活跃细胞,分裂细胞和成串细胞。不同种类的细胞采用不同的追踪算法。一种特殊的后向追踪可以修改和纠正前向追踪里出现的错误,并以轨迹图的方式显示最后的追踪结果。  相似文献   

2.
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。  相似文献   

3.
一种基于水平集的前列腺超声图像自动分割算法   总被引:1,自引:1,他引:0  
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.  相似文献   

4.
使用超声成像进行子宫节育环检查工作已在我国广泛地开展,利用图像识别技术进行计算机辅助诊断对于减轻检查人员工作负担意义十分明显,其中图像分割部分的主要目标则是快速地全自动分割开图中的几个主要器官及节育环。本研究提出了一种快速的全自动子宫图像分割算法。该算法包括以下三个主要步骤:首先运用BP神经网络处理图像整体灰度分布获取基准分割阈值;其后使用超声图像斑点噪声统计特征进行同质区域判别,并根据局部灰度分布自适应调整分割阈值;最后使用数学形态学算子对分割效果做进一步的改善。基于由1200幅超声子宫图像组成的图像库,对所提算法与最大类别方差法、SNAKE活动轮廓模型等数种常用分割算法进行了性能比较,实验结果表明所提算法在速度与准确程度两方面均表现良好,平均耗时为0.93s/幅,准确程度达到了94%。本算法无需人工干预,分割速度快,分割准确程度能够被临床医生所接受,可以用作超声子宫图像辅助诊断系统的图像分割部分,具有很好的应用前景。  相似文献   

5.
医学图像中解剖结构和相关诊断信息的提取有着极为重要的意义,但目前的分割算法大都需要借助专家的干预和监控,寻求一种全自动分割的方法变得日益重要。人工生命的方法有助于人们了解生物学规律,并且在机器人、计算机图形学等方面得到了成功应用。主要介绍基于人工生命的方法在医学图像自动分割领域的初步应用。  相似文献   

6.
有限混合(FM)模型已经广泛地应用于图像分割,但是由于没有考虑空间信息,导致分割的结果对噪声很敏感,分割出的区域存在很多杂散的孤立点。本文Gibbs随机场理论的指导下,将空间信息引入FM模型,提出了改进的脑部MR图像分割算法。此外,由树形K平均聚类来估计初始参数,实现了全自动的图像分割。本研究进行了仿真MR图像和真实MR图像的分割实验,定量的数据分析表明,我们所提的改进算法对噪声不敏感,可以更精确地将脑部MR图像标记为灰质、白质与脑脊液三种组织类型。  相似文献   

7.
为解决人工标定的繁琐、非客观等问题,本文提出一种基于支持向量机的全自动分割算法。该算法采用K-means对图像像素进行聚类,根据聚类结果和聚类中心对图像进行标准化处理,并进行图像分割提取感兴趣区域。根据训练样本训练支持向量得出分类模型,将感兴趣区域的像素分为边界点和非边界点。然后将边界点再次分类为管腔-内膜边界点和中膜-外膜边界点。最后采用启发式搜索对分类结果进行甄别,去除错分类的像素点。本文采用80幅颈动脉超声图像进行实验,比较实验结果与金标准,内中膜厚度平均绝对误差为(46.08±23.50)μm,平均每幅图像处理时间为0.88 s。实验结果表明全自动分割算法具有快速、全自动等特点,测量结果与金标准具有较高的一致性,能满足临床应用的实际要求。  相似文献   

8.
目的:免疫组化彩色细胞图像中阳性细胞的自动分割提取有着重要的临床意义。本文结合三种分割算法的特点,研究实现免疫组化彩色细胞图像的自动分割,提取图像中的阳性细胞。方法:(1)采用OTSU方法在灰度的基础上对免疫组化彩色细胞进行预分割,去除无关背景。(2)使用K-聚类算法,对彩色细胞图像进行彩色分类筛选出阳性细胞和阴性细胞,并对所得阳性细胞图像进行腐蚀,以获取阳性细胞图像的种子。(3)使用区域生长算法对阳性细胞种子进行区域增长。获取完整的阳性细胞图。结果:准确分割出图像中的阳性细胞。结论:该自动分割方法可用于后续的阳性细胞自动计数,辅助医生诊断疾病。  相似文献   

9.
核磁共振图像的自动图像分割和组织分类至今仍是一个有待解决的问题。在理想的情况下,各类组织的灰度呈正态分布;但受RF线圈、MR设备的操作环境等的影响,图像的灰度均匀性变差,相当于在增益场上叠加了一个偏移场,使信号产生混淆。作者采用“适配方割算法”,通过计算有偏场,并对图像进行灰度校正,可以达到全自动地分割脑组织图像。  相似文献   

10.
提出一种新的灰度和形状信息相结合的全自动同模态医学图像非刚性配准-分割算法,将欧氏距离表示的形状信息融入基于灰度的配准算法中,构造出新的代价函数.该算法在医学图像多目标分割的应用中,能够较好地完成灰度相近、边缘模糊、间距较小的不同结构的分割.对5组真实脑部MRI图像进行分割脑深层灰质结构的实验,结果表明,本算法优于基于灰度信息的图像配准算法.  相似文献   

11.
三维医学图像序列的自动连续分割   总被引:2,自引:0,他引:2  
我们针对医学辅助诊断系统中从M R图像分割脑肿瘤的问题,改进了区域竞争算法,并利用它实现了医学图像序列间的连续自动分割,特别是脑肿瘤的分割和脑膜瘤的自动识别。模糊化区域竞争算法是为了更好的适应医学图像的模糊与不均匀的特点,而用区域增长做初始化可以给区域竞争提供用来竞争和合并的过分割区域。为了实现医学图像序列的自动连续分割,每一副切片的分割结果都会被用作初始化下一张切片;并且我们根据脑膜瘤的特点实现了它的自动识别。实验表明,我们的自动分割算法对仿真脑图像和真实脑图像均有较好的分割精度,并能满足系统对分割快速性的需要。  相似文献   

12.
We present a fully automated cerebrum segmentation algorithm for full three-dimensional sagittal brain MR images. First, cerebrum segmentation from a midsagittal brain MR image is performed utilizing landmarks, anatomical information, and a connectivity-based threshold segmentation algorithm as previously reported. Recognizing that cerebrum in laterally adjacent slices tends to have similar size and shape, we use the cerebrum segmentation result from the midsagittal brain MR image as a mask to guide cerebrum segmentation in adjacent lateral slices in an iterative fashion. This masking operation yields a masked image (preliminary cerebrum segmentation) for the next lateral slice, which may truncate brain region(s). Truncated regions are restored by first finding end points of their boundaries, by comparing the mask image and masked image boundaries, and then applying a connectivity-based algorithm. The resulting final extracted cerebrum image for this slice is then used as a mask for the next lateral slice. The algorithm yielded satisfactory fully automated cerebrum segmentations in three-dimensional sagittal brain MR images, and had performance superior to conventional edge detection algorithms for segmentation of cerebrum from 3D sagittal brain MR images.  相似文献   

13.
In this paper a novel, automated CT marker segmentation technique for image registration is described. The technique, which is based on analysing each CT slice contour individually, treats the cross sections of the external markers as protrusions of the slice contour. Knowledge-based criteria, using the shape and dimensions of the markers, are defined to enable marker identification and segmentation. Following segmentation, the three-dimensional (3D) markers' centroids are localized using an intensity-weighted algorithm. Finally, image registration is performed using a least-squares fit algorithm. The technique was applied to both simulated and patient studies. The patients were undergoing 131I-mIBG radionuclide therapy with each study comprising several 99mTc single photon emission computed tomography (SPECT) scans and one CT marker scan. The mean residual 3D registration errors (+/- 1 SD) computed for the simulated and patient studies were 1.8 +/- 0.3 mm and 4.3 +/- 0.5 mm respectively.  相似文献   

14.
"虚拟中国人男性一号"多模态图像配准   总被引:1,自引:0,他引:1  
目的:解决“虚拟中国人男性一号”CT图像、MRI图像与断层切削图像之间的多模态图像配准问题。材料和方法:根据这三种图像的特点,选择CT图像为基准图像,在对MRI图像进行配准时,通过求解两幅图像梯度特征的最大互信息,搜索出最佳配准参数;在对断层切削图像进行配准时,采用基于解剖结构特征提取的配准方法获取最佳配准参数:最后.根据所得配准参数对待配图进行变换,从而达到配准目的。结果:对头部三种模态图像数据集进行了配准,与高精度手工分割图像数据集进行对比,配准正确率达到95.8%。结论:配准结果准确,解决了“虚拟中国人男性一号”多模态图像配准问题,为数字化虚拟人多模态图像配准提供了参考。  相似文献   

15.
基于雅可比-傅里叶矩的细胞模式识别   总被引:2,自引:2,他引:0  
细胞学检查自动化是生物医学工程研究的重要课题。文中介绍了雅可比一傅里叶矩,它具有多畸不变性。多种临床细胞涂片模式识别表明,该矩不但是一种高度浓缩的图像特征,还有较好的抗畸变、抗噪声性能,可用于快速准确的细胞学自动化识别。  相似文献   

16.
本文研究人体CT图像中腰椎松质骨骨小梁数据集的获取、分割及三维重建的技术方法.利用高速螺旋CT(HCT)技术和图像数码转换技术,获取了人体腰椎松质骨CT连续图像数据集,将切片图像输入二维图像处理软件进行分割,提取感兴趣区域后输入三维重建软件进行三维重建与定性分析.重建后的松质骨三维立体图像呈均匀、致密的立体网状结构,骨小梁连接清晰可见.提示利用现有软件和技术可重建松质骨骨小梁三维立体图像和诊断骨质疏松.  相似文献   

17.
Fast segmentation of bone in CT images using 3D adaptive thresholding   总被引:1,自引:0,他引:1  
Fast bone segmentation is often important in computer-aided medical systems. Thresholding-based techniques have been widely used to identify the object of interest (bone) against dark backgrounds. However, the darker areas that are often present in bone tissue may adversely affect the results obtained using existing thresholding-based segmentation methods. We propose an automatic, fast, robust and accurate method for the segmentation of bone using 3D adaptive thresholding. An initial segmentation is first performed to partition the image into bone and non-bone classes, followed by an iterative process of 3D correlation to update voxel classification. This iterative process significantly improves the thresholding performance. A post-processing step of 3D region growing is used to extract the required bone region. The proposed algorithm can achieve sub-voxel accuracy very rapidly. In our experiments, the segmentation of a CT image set required on average less than 10 s per slice. This execution time can be further reduced by optimizing the iterative convergence process.  相似文献   

18.
本文介绍一种计算机辅助检测CT层厚的方法,该方法通过编程对Catphan412体模中的层厚插件的图像进行分析,从而确定CT机的层厚。经过实验证明,该方法准确、客观,完全可以代替手工检测。  相似文献   

19.
In this paper, we report on two methods for semiautomatic three-dimensional (3-D) prostate boundary segmentation using 2-D ultrasound images. For each method, a 3-D ultrasound prostate image was sliced into the series of contiguous 2-D images, either in a parallel manner, with a uniform slice spacing of 1 mm, or in a rotational manner, about an axis approximately through the center of the prostate, with a uniform angular spacing of 5 degrees. The segmentation process was initiated by manually placing four points on the boundary of a selected slice, from which an initial prostate boundary was determined. This initial boundary was refined using the Discrete Dynamic Contour until it fit the actual prostate boundary. The remaining slices were then segmented by iteratively propagating this result to an adjacent slice and repeating the refinement, pausing the process when necessary to manually edit the boundary. The two methods were tested with six 3-D prostate images. The results showed that the parallel and rotational methods had mean editing rates of 20% and 14%, and mean (mean absolute) volume errors of -5.4% (6.5%) and -1.7% (3.1%), respectively. Based on these results, as well as the relative difficulty in editing, we conclude that the rotational segmentation method is superior.  相似文献   

20.
Shape-based grey-level image interpolation.   总被引:6,自引:0,他引:6  
The three-dimensional (3D) object data obtained from a CT scanner usually have unequal sampling frequencies in the x-, y- and z-directions. Generally, the 3D data are first interpolated between slices to obtain isotropic resolution, reconstructed, then operated on using object extraction and display algorithms. The traditional grey-level interpolation introduces a layer of intermediate substance and is not suitable for objects that are very different from the opposite background. The shape-based interpolation method transfers a pixel location to a parameter related to the object shape and the interpolation is performed on that parameter. This process is able to achieve a better interpolation but its application is limited to binary images only. In this paper, we present an improved shape-based interpolation method for grey-level images. The new method uses a polygon to approximate the object shape and performs the interpolation using polygon vertices as references. The binary images representing the shape of the object were first generated via image segmentation on the source images. The target object binary image was then created using regular shape-based interpolation. The polygon enclosing the object for each slice can be generated from the shape of that slice. We determined the relative location in the source slices of each pixel inside the target polygon using the vertices of a polygon as the reference. The target slice grey-level was interpolated from the corresponding source image pixels. The image quality of this interpolation method is better and the mean squared difference is smaller than with traditional grey-level interpolation.  相似文献   

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