基于形态学的均值平移算法的神经元干细胞图像的自动分割

在神经元干细胞的图像分析中,准确快速的图像分割是干细胞分化增值自动追踪系统的基础。为了准确地分割低对比度的灰度神经元干细胞图像,本研究提出一种基于形态学运算和均值平移算法的神经元干细胞分割方法,称其为形态学的均值平移算法。此算法可以快速地获得任意形状细胞的图像,并且能检测到图像中多连接边缘不封闭的细胞。将此方法应用于神经元干细胞序列图像分割中并且将其与门限分割、水线分割和活动轮廓进行对比。实验结果证明,与其它的方法相比,此方法获得的细胞分割形状更接近于真实细胞的形状,并且能获得或接近于原始图像中准确的独立细胞数目。此方法可以获得正确的分割结果,为进一步图像处理奠定了良好的基础。     

神经元干细胞图像的全自动分割与追踪

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。     

基于活动轮廓模型的彩色白细胞图像自动分割方法研究

为解决血液白细胞显微图像自动识别中的图像分割问题,提出了一种基于活动轮廓的彩色白细胞图像自动分割方法,首先在Hue,Saturation,Intensitv（HSI）彩色空间中运用聚类分割得到细胞核,从而得到细胞所在的位置,然后用流域算法得到细胞大致的轮廓,最后将此轮廓作为初始轮廓,用梯度矢量流（GVF）外力及来自全局信息的区域力驱动,结合彩色信息,使得轮廓收敛于真实的细胞边界。实验结果表明,此方法能精确、有效地分割出单个以及部分重叠白细胞区域。     

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。     

一种基于Snake模型的脑部CT图像分割新算法

针对目前传统的Snake模型图像分割算法的力场捕捉范围小、对初始轮廓的选取敏感以及对轮廓曲线难以收敛到 细小深凹边界的缺陷,提出一种基于Snake 模型的脑部CT图像分割新算法。算法首先运用Canny 边缘算子对图像进行 边缘检测,将边缘检测图像叠加到原始图像上,然后再运用Snake模型和梯度向量流（GVF）Snake模型分别对叠加图像进 行分割。实验结果表明,该算法克服了传统Snake 模型和GVF Snake 模型因边缘轮廓不清晰造成的漏分割情况,防止了 GVF Snake模型由于GVF力场的相互作用所造成的过分割现象,同时,还能促使轮廓线收敛到细小深凹边界,提高定位精 度,具有更好的分割效果。     

股骨CT图像轮廓跟踪方法

目的 为重建和测量股骨的解剖结构，需要大量地读取CT图像的信息，以获得股骨轮廓的坐标值。方法本研究采用直方图阈值图像分割、Kirsh边缘提取法获得股骨的二值化轮廓图像。结果轮廓坐标的提取应用了“迷宫”边缘跟踪算法。结论本方法可大量、快捷、正确地提取图像轮廓信息。     

基于MATLAB视网膜视锥细胞的图像处理

背景：视网膜自适应光学成像系统所获取的视锥细胞图像具有灰度分布相对集中、亮点边缘比较模糊、存在伪轮廓及边缘相粘连的特点,寻找一种适合视锥细胞图像的处理算法来获取视网膜视锥细胞清晰轮廓成为今后工作的重点内容。 目的：采用MATLAB图像处理工具箱对视锥细胞图像进行边缘提取,获取其清晰的边缘轮廓。 方法：对30例正常受试者不同部位视锥细胞图像进行预处理、边缘提取和形态学处理,获取视锥细胞图像清晰的边缘轮廓;对处理后的图像进行细胞计数,并分析视锥细胞密度分布特性,进而来验证图像处理算法应用于视锥细胞分布特性研究的可行性。 结果与结论：获取了清晰的视网膜视锥细胞图像轮廓;从结果来看,随着远离黄斑中心凹,细胞密度呈现出降低的趋势,从黄斑中心凹偏0.5°到1°范围内,视网膜视锥细胞密度下降最快。结果提示：实验所设计的图像处理算法在研究视网膜视锥细胞分布特性方面是可行的。     

基于改进的CV-RSF模型的甲状腺结节超声图像自适应分割算法

目的甲状腺结节超声图像的精确分割对甲状腺结节的良恶性诊断尤为重要。目前,对于甲状腺结节超声图像的分割,有学者提出利用主动轮廓模型分割算法,但是由于活动轮廓分割算法需要手动设置迭代次数,未实现模型的自适应性。因此,本文提出了一种基于改进的无边缘主动轮廓-局部区域可控的拟合(Chan-Vese-region scalable fitting,CV-RSF)模型的甲状腺结节超声图像自适应分割算法。方法选取南京同仁医院12例患者的甲状腺结节超声图像用于实验。首先,在无边缘主动轮廓(Chan-Vese,CV)模型中,引入一个基于梯度的边缘引导函数,根据面积变化率,自适应地获取甲状腺结节的粗分割轮廓;然后,将粗分割轮廓作为局部区域可控的拟合(region-scalable fitting,RSF)模型的初始轮廓,并根据面积变化率,自适应地获取甲状腺结节最终分割结果。将改进模型分割的结果与CV模型、RSF模型分割的结果进行比较,并分析甲状腺结节边缘清晰度对分割结果的影响。结果本文模型算法分割结果的平均迭代次数、平均面积重叠率、平均Hausdorff分别达到了134、90.34%、9.77,均优于CV模型、RSF模型的分割算法。结论该算法有效地分割出边缘清晰和不清晰的甲状腺结节超声图像,并解决手动设置迭代次数的问题,从而实现甲状腺结节的有效、准确、自动分割。     

股骨CT图像的边缘提取

为重建和测量股骨的解剖结构,需要大量地读取CT图像的信息,以获得股骨轮廓的坐标值.本研究采用直方图阈值图像分割、Kirsh边缘提取方法获得股骨的二值化轮廓图像.轮廓的提取应用了"迷宫"边缘跟踪算法.本方法可大量、快捷、正确地提取图像轮廓信息.     

生物细胞图像分割技术的进展

阐述了小波变换、遗传算法、模糊数学、神经网络，数学形态学等生物细胞图像分割算法以及边缘检测，区域分割等传统图像分割算法为主的生物细胞图像分割技术的发展现状，指明了生物细胞图像本身具有的复杂性，多样性，各自差异等属性是实现生物细胞图像全自动分割的难点，只有彻底结合生物视觉特性数学模型算法的研究和应用，才能命名生物细胞全自动分割成为可能。     

Self-organizing map-based multi-thresholding on neural stem cells images

Automatic segmentation and tracking systems can be useful tools for biologists to monitor and understand the proliferation and the differentiation of neural stem cells. This paper applied the self-organizing map-based multi-thresholding on the neural stem cells images. Using local variance as the local spatial feature and quadtree decomposition as the sub-sampling method, inner-cell regions, cell borders and background can be roughly classified. Based on these results, proper foreground and background seeds were constructed for the seeded watershed segmentation and every single cell in a cell cluster can be segmented correctly. The results were also compared to the seeded watershed segmentation based on regional maxima method.     

Automatic Intracranial Space Segmentation for Computed Tomography Brain Images

Craniofacial disorders are routinely diagnosed using computed tomography imaging. Corrective surgery is often performed early in life to restore the skull to a more normal shape. In order to quantitatively assess the shape change due to surgery, we present an automated method for intracranial space segmentation. The method utilizes a two-stage approach which firstly initializes the segmentation with a cascade of mathematical morphology operations. This segmentation is then refined with a level-set-based approach that ensures that low-contrast boundaries, where bone is absent, are completed smoothly. We demonstrate this method on a dataset of 43 images and show that the method produces consistent and accurate results.     

Three-dimensional mapping of lingual myoarchitecture by diffusion tensor MRI

This study was performed to assess the feasibility of investigating the complex lingual myoarchitecture through segmentation of muscles from diffusion tensor imaging (DTI) data. The primary eigenvectors were found to be adequate for delineating the superior and inferior longitudinalis, genioglossus, and hyoglossus. The tertiary eigenvector orientations effectively revealed the homogeneous and systematic change of muscle orientation in the tongue core. In the longitudinalis near the tongue tip, the secondary eigenvectors were oriented in the radial direction. Lingual muscles were segmented using two methods: modified directional correlation (DC) and tensor coherence (TC) methods. The DC method, based on one eigenvector, was found to be inadequate for lingual muscle segmentation, whereas the TC method, based on the tensor shape and orientation, was used successfully to segment most lingual muscles. The segmentation result was used to report the diffusion tensor properties of individual lingual muscles. Also found was a continuous change in skewness of the intrinsic tongue core from negative in the anterior region to positive in the posterior region. DTI and the proposed segmentation method provide an adequate means of imaging and visualizing the complex, compartmentalized musculature of the tongue. The potential for in vivo research and clinical applications is demonstrated.     

医学显微图像分割方法研究进展

医学显微图像分割是医学图像处理中的一个经典难题.针对近年来出现的新方法、新理论,对各种分割方法进行了系统论述,主要包括基于数学形态学方法、神经网络分割、模糊分割、小波分析、遗传算法、统计方法和基于特定模型等方法的图像分割.由于显微图像的复杂性,采用单一方法很难准确分割,故对混合方法也作了一定论述.文中还简要讨论了各种方法的特点和局限性.同时对分割的评价体系也做了简要论述.     

Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening

Cancer screening with magnetic resonance imaging (MRI) is currently recommended for very high risk women. The high variability in the diagnostic accuracy of radiologists analyzing screening MRI examinations of the breast is due, at least in part, to the large amounts of data acquired. This has motivated substantial research towards the development of computer-aided diagnosis (CAD) systems for breast MRI which can assist in the diagnostic process by acting as a second reader of the examinations. This retrospective study was performed on 184 benign and 49 malignant lesions detected in a prospective MRI screening study of high risk women at Sunnybrook Health Sciences Centre. A method for performing semi-automatic lesion segmentation based on a supervised learning formulation was compared with the enhancement threshold based segmentation method in the context of a computer-aided diagnostic system. The results demonstrate that the proposed method can assist in providing increased separation between malignant and radiologically suspicious benign lesions. Separation between malignant and benign lesions based on margin measures improved from a receiver operating characteristic (ROC) curve area of 0.63 to 0.73 when the proposed segmentation method was compared with the enhancement threshold, representing a statistically significant improvement. Separation between malignant and benign lesions based on dynamic measures improved from a ROC curve area of 0.75 to 0.79 when the proposed segmentation method was compared to the enhancement threshold, also representing a statistically significant improvement. The proposed method has potential as a component of a computer-aided diagnostic system.     

Microscopic Cell Nuclei Segmentation Based on Adaptive Attention Window

This paper presents an adaptive attention window (AAW)-based microscopic cell nuclei segmentation method. For semantic AAW detection, a luminance map is used to create an initial attention window, which is then reduced close to the size of the real region of interest (ROI) using a quad-tree. The purpose of the AAW is to facilitate background removal and reduce the ROI segmentation processing time. Region segmentation is performed within the AAW, followed by region clustering and removal to produce segmentation of only ROIs. Experimental results demonstrate that the proposed method can efficiently segment one or more ROIs and produce similar segmentation results to human perception. In future work, the proposed method will be used for supporting a region-based medical image retrieval system that can generate a combined feature vector of segmented ROIs based on extraction and patient data.     

Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system

In previous research, we have developed a computer-aided detection (CAD) system designed to detect masses in mammograms. The previous version of our system employed a simple but imprecise method to localize the masses. In this research, we present a more robust segmentation routine for use with mammographic masses. Our hypothesis is that by more accurately describing the morphology of the masses, we can improve the CAD system's ability to distinguish masses from other mammographic structures. To test this hypothesis, we incorporated the new segmentation routine into our CAD system and examined the change in performance. The developed iterative, linear segmentation routine is a gray level-based procedure. Using the identified regions from the previous CAD system as the initial seeds, the new segmentation algorithm refines the suspicious mass borders by making estimates of the interior and exterior pixels. These estimates are then passed to a linear discriminant, which determines the optimal threshold between the interior and exterior pixels. After applying the threshold and identifying the object's outline, two constraints on the border are applied to reduce the influence of background noise. After the border is constrained, the process repeats until a stopping criterion is reached. The segmentation routine was tested on a study database of 183 mammographic images extracted from the Digital Database for Screening Mammography. Eighty-three of the images contained 50 malignant and 50 benign masses; 100 images contained no masses. The previously developed CAD system was used to locate a set of suspicious regions of interest (ROIs) within the images. To assess the performance of the segmentation algorithm, a set of 20 features was measured from the suspicious regions before and after the application of the developed segmentation routine. Receiver operating characteristic (ROC) analysis was employed on the ROIs to examine the discriminatory capabilities of each individual feature before and after the segmentation routine. A statistically significant performance increase was found in many of the individual features, particularly those describing the mass borders. To examine how the incorporation of the segmentation routine affected the performance of the overall CAD system, free-response ROC (FROC) analysis was employed. When considering only malignant masses, the FROC performance of the system with the segmentation routine appeared better than the previous system. When detecting 90% of the malignant masses, the previous system achieved 4.9 false positives per image (FPpI) compared to the post-segmentation system's 4.2 FPpI. At 80% sensitivity, the respective FPpI were 3.5 and 1.6.     

Segmentation and Tracking of Neural Stem Cell

INTRODUCTION The birth of new neurons from neural stem cells,a process called neurogenesis,has been seen in adult brains from both animals and humans〔1〕.However,little isknown about the basic regulatory mechanisms of neurogenesis.In order to under-stand this regeneration of brain cells,cultured cells are studied.In this way,someproperties of neural stem cells as they develop over time can be discovered.For thispurpose efficient methods for tracking cells in cultures are needed.There ar…