基于改进活动窄带模型的冠状动脉CT血管造影图像分割

冠状动脉分割与狭窄分级、斑块检测等密切相关,是血管病变研究中的重要步骤。针对血管灰度不均和对比度低等问题,提出了一种基于活动窄带和符号压力函数水平集的CT血管造影冠状动脉分割方法。首先对初始轮廓做形态学膨胀和腐蚀运算,以构建活动窄带限定轮廓曲线的演化区域;其次在活动窄带区域内构造局部符号压力函数,用水平集算法使初始轮廓收敛至准确轮廓;最后利用形态学闭运算平滑曲线。通过利用活动窄带将图像区域局部化,降低了计算复杂度,克服了灰度不均匀性,促进轮廓曲线演化到细小的血管末梢和狭窄区域。实验结果表明,与传统的分割方法相比,能够更加有效准确地分割出冠状动脉,为血管病变的研究提供支持。     

RSF模型的优化及其在MRI脑肿瘤分割中的应用

磁共振成像(MRI)具有图像模糊,灰度不均等特点,其分割问题一直都是研究的热点和难点。可变区域拟合(RSF)能量模型是一种较新的区域活动轮廓模型,可用于灰度不均匀图像的分割。然而,RSF模型设定的水平集函数(LSF)不适合初始轮廓内外灰度分布不同的环境,应用于整体灰度环境复杂的脑肿瘤MRI图像时,通常得不到理想的分割结果。构建新的LSF,并辅以mean shift平滑算法可使其更适用于肿瘤图像的分割,使新模型具有更好的收敛性和目标指向性。利用优化后的模型进行一系列实验,其结果表明:该算法鲁棒性强,可以快速、准确地分割出MRI图像中的脑肿瘤,具有显著的临床意义。     

基于改进C-V模型的肿瘤CT图像分割

针对传统C-V模型演化速度慢和不能很好分割灰度不均匀图像的缺点,从两个方面进行了改进。首先采用一个新颖的基于局部梯度的模型,使C-V模型初始轮廓曲线快速移到目标边界附近,大大缩短了演化时间;其次,结合GVF模型从两个方向指向目标边界的特点,为C-V模型的速度方程添加一个自适应速度调节项,使模型收敛于真实边界。通过肝脏肿瘤CT图像的分割,验证该方法是有效的。     

用于面诊的嘴巴定位算法研究

针对嘴巴区域特征的精确定位对于中医面诊客观化的研究具有重要意义,提出一种基于双肤色模型、椭圆拟合与改进C-V水平集模型相结合的分割嘴巴的方法。考虑到肤色在像素空间邻域与灰度值域的平滑相似性,首先给出一种二维伽马函数的自适应光照补偿法,提升非均匀光照下肤色聚类的稳定性,进而利用根据实验确定的双肤色模型,进行肤色检测,并采用数学形态学方法去除噪声等影响,Sobel方法提取轮廓;然后根据所得的边缘和初步轮廓利用直接最小二乘椭圆拟合法提取人脸区域;最后采用改进C-V水平集模型对嘴部区域进行分割。实验结果表明,采用该算法能够得到更好的分割效果,满足中医面诊图像分割要求,为面部五官的进一步分割和检测奠定基础。     

基于混合水平集的脑胶质瘤分割方法

全面考虑脑胶质瘤分割图像的边界信息和区域信息,在水平集的基础上,将基于边缘检测的活动轮廓模型(GAC模型)和局部图像拟合模型(LIF模型)相结合,提出一种混合水平集的分割方法。首先,对脑胶质瘤MR图像进行预处理,采用C-V模型提取脑组织;然后,创建混合水平集模型,对脑组织图像中的脑胶质瘤进行分割。实验证明,本研究的分割方法可以简化水平集符号距离函数的正则化过程,并且可有效克服GAC模型在弱边缘或离散边缘处产生的边界泄漏的问题,从而取得较好的分割结果。     

一种新的舌癌图像快速自动分割方法

目的:研究一种新的舌癌图像自动分割算法以实现对舌癌肿瘤的快速准确分割。方法:通过引入一种基于局部均方差的自适应尺度算子实现演化曲线在演化过程中的自动调整,从而更高效率地向真实目标边界运动,并且克服舌癌肿瘤图像中目标边界不清和图像灰度不均匀等不良因素带来的影响。此外,为加快曲线的收敛速度,本文提出了一种新的能量项评估演化曲线轮廓内部和轮廓外部区域灰度的分布差异,以此引导曲线自适应地调整演化速度,减少完成分割任务所需的迭代次数。结果:使用本方法对22幅舌癌肿瘤MRI图像进行分割,分割结果与真实结果之间的重叠率Dice值为0.82,豪斯多夫距离HD值为1.732 mm。结论:将本文算法与其它现有的几种活动轮廓模型进行定性和定量对比分析,实验结果表明本文算法在对细节及弱边缘灰度的处理上表现更加优异,可用于舌癌肿瘤的精确分割,为临床分析提供辅助信息。     

基于全局和局部相关熵活动轮廓模型的超声图像分割算法

局部相关熵K均值(LCK)模型在非高斯噪声和图像灰度不均匀时具有较好的图像分割效果,但是其计算复杂度较高,收敛较慢。针对该问题,本研究将局部相关熵能量项和全局相关熵能量项结合,提出了全局与局部相关熵K均值(GLCK)图像分割算法。其中局部相关熵力在目标边界附近起主导作用,用来吸引水平集函数曲线到达目标边界,而全局相关熵力在远离目标边界处起主导作用。对超声医学图像和人工合成图像进行了图像分割实验,并同LCK等模型进行了对比,结果表明提出的GLCK模型具有更好的鲁棒性和图像分割精度,并且计算时间也显著减少。另一方面GLCK模型对于噪声和模糊边界影响严重的超声医学图像,具有好的分割效果。     

基于全局和区域可伸缩拟合局部熵活动轮廓模型的超声图像分割

针对区域可伸缩拟合局部熵(region-scalable fitting based on local entropy,RSF_LE)模型图像分割效率低的问题,本研究提出一种改进的RSF_LE模型。定义带有加权局部灰度拟合项以及辅助的加权全局灰度拟合项的能量泛函,其中加权局部灰度拟合项负责对目标边界附近的轮廓进行诱导,使其靠近目标物边界,加权全局灰度拟合项利用图像的全局信息来引导远离目标的轮廓向目标靠拢,该方法可以克服传统的RSF_LE模型分割算法效率低下的问题,并提高了该方法的鲁棒性。     

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。     

交互式GACV模型的肿瘤图像分割

目的:提取医学图像中肿瘤区域,用以测量肿瘤体积问题。方法:提出一种基于GACV(Geodesic-Aided C-Vmethod)的交互式模型。该模型首先人工选取感兴趣区域,并在区域内设定初始水平集与肿瘤内部种子点,然后在感兴趣区域上应用将图像梯度边缘信息与图像区域灰度特性统一到同一分割中的GACV模型,得到肿瘤的粗分割结果。最后为去除目标内外孔洞,提出一种无损边缘的膨胀搜索算法,作为细分割。结果:将该模型应用于不同形状的肿瘤图像中,能成功检测肿瘤轮廓。通过实验与其它活动轮廓分割方法结果对比,结果显示该模型在准确分割肿瘤边界与分割算法耗时方面均具有良好表现。结论:本文提出的分割方法能高效率、准确识别肿瘤区域。     

A dual-stage method for lesion segmentation on digital mammograms

Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures. In this article, we present a method for automatic delineation of lesion boundaries on digital mammograms. This method utilizes a geometric active contour model that minimizes an energy function based on the homogeneities inside and outside of the evolving contour. Prior to the application of the active contour model, a radial gradient index (RGI)-based segmentation method is applied to yield an initial contour closer to the lesion boundary location in a computationally efficient manner. Based on the initial segmentation, an automatic background estimation method is applied to identify the effective circumstance of the lesion, and a dynamic stopping criterion is implemented to terminate the contour evolution when it reaches the lesion boundary. By using a full-field digital mammography database with 739 images, we quantitatively compare the proposed algorithm with a conventional region-growing method and an RGI-based algorithm by use of the area overlap ratio between computer segmentation and manual segmentation by an expert radiologist. At an overlap threshold of 0.4, 85% of the images are correctly segmented with the proposed method, while only 69% and 73% of the images are correctly delineated by our previous developed region-growing and RGI methods, respectively. This resulting improvement in segmentation is statistically significant.     

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。     

Multiscale active contour model for vessel segmentation

This paper presents a novel multiscale active contour model for vessel segmentation. The model is based on accurate analysis of the vessel structure in the image. According to different scale response of the eigenvalues of local second order derivative (Hessian matrix), a new vessel region information function, which shows a valid estimation of the vesselness measure, is defined. We introduce the posteriori probability estimation into the active contours framework and design a new objective function. The defined objective function is minimized using the variational method, and a new region-based external force is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. This active contour model combines the obtained region-based and conventional boundary-based force, which aims at finding more accurate vessel edges even when the vessel branches are low contrast or blurry. Furthermore, the proposed model is implemented by an implicit method of level set framework, the solution of which is steady and suitable for various topology changes. Moreover, two new speed functions for vessel segmentation in the level set method are presented, one for fast marching and the other for a narrow-band algorithm. The vessel segmentation experiments compared with previous geometric active contour models are shown on several medical images. The experimental results demonstrate the performance of our approach.     

Liver Hydatid CT Image Segmentation Using Smoothed Bayesian Classification Method and Modified Parametric Active Contour Model

Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.     

基于改进Mask R-CNN的牙齿识别与分割

针对当前的研究方法在牙齿全景X光片上提取的信息较为单一,而未曾考虑将牙齿的类别信息与形状位置信息融合提取的问题,提出一种实例分割方法同时实现牙齿识别与分割。主要通过融合跳跃结构和SE（Squeeze and Excitation）模块对Mask R-CNN实例分割模型中的分割分支进行改进,并以牙齿功能与FDI牙位两种类别编码方式,采用400张牙齿全景X光片数据进行实验仿真。实验结果表明改进后的模型相比于其他模型,可以同时有效地进行牙齿分类和分割,实现牙齿类别、形状、位置信息的融合提取,改善了Mask R-CNN实例分割模型在分割分支中语义信息提取不足的问题。     

Endocardial Border Detection in Cardiac Magnetic Resonance Images Using Level Set Method

Segmentation of the left ventricle in MRI images is a task with important diagnostic power. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction. This evaluation requires the segmentation of the left ventricle contour. In this paper, we propose a new method for automatic detection of the endocardial border in cardiac magnetic resonance images, by using a level set segmentation-based approach. To initialize this level set segmentation algorithm, we propose to threshold the original image and to use the binary image obtained as initial mask for the level set segmentation method. For the localization of the left ventricular cavity, used to pose the initial binary mask, we propose an automatic approach to detect this spatial position by the evaluation of a metric indicating object’s roundness. The segmentation process starts by the initialization of the level set algorithm and ended up through a level set segmentation. The validation process is achieved by comparing the segmentation results, obtained by the automated proposed segmentation process, to manual contours traced by tow experts. The database used was containing one automated and two manual segmentations for each sequence of images. This comparison showed good results with an overall average similarity area of 97.89%.     

Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour

Knowing the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy, a commonly used prostate cancer treatment method. The prostate boundary must be segmented before a dose plan can be obtained. However, manual segmentation is arduous and time consuming. This paper introduces a semi-automatic segmentation algorithm based on the dyadic wavelet transform (DWT) and the discrete dynamic contour (DDC). A spline interpolation method is used to determine the initial contour based on four user-defined initial points. The DDC model then refines the initial contour based on the approximate coefficients and the wavelet coefficients generated using the DWT. The DDC model is executed under two settings. The coefficients used in these two settings are derived using smoothing functions with different sizes. A selection rule is used to choose the best contour based on the contours produced in these two settings. The accuracy of the final contour produced by the proposed algorithm is evaluated by comparing it with the manual contour outlined by an expert observer. A total of 114 2D TRUS images taken for six different patients scheduled for brachytherapy were segmented using the proposed algorithm. The average difference between the contour segmented using the proposed algorithm and the manually outlined contour is less than 3 pixels.     

Segmentation of carotid artery in ultrasound images: method development and evaluation technique

Segmentation of carotid artery lumen in two-dimensional and three-dimensional ultrasonography is an important step in computerized evaluation of arterial disease severity and in finding vulnerable atherosclerotic plaques susceptible to rupture causing stroke. Because of the complexity of anatomical structures, noise as well as the requirement of accurate segmentation, interactions are necessary between observers and the computer segmentation process. In this paper a segmentation process is described based on the deformable model method with only one seed point to guide the initialization of the deformable model for each lumen cross section. With one seed, the initial contour of the deformable model is generated using the entropy map of the original image and mathematical morphology operations. The deformable model is driven to fit the lumen contour by an internal force and an external force that are calculated, respectively, with geometrical properties of deformed contour and with the image gray level features. The evaluation methodology using distance-based and area-based metrics is introduced in this paper. A contour probability distribution (CPD) method for calculating distance-based metrics is introduced. The CPD is obtained by generating contours of the lumen using a set of possible seed locations. The mean contour can be compared to a manual outlined contour to provide accuracy metrics. The variance computed from the CPD can provide metrics of local and global variability. These metrics provide a complete performance evaluation of an interactive segmentation algorithm and a means for comparing different algorithm settings.     

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。     

基于活动轮廓模型的冠状动脉血管段运动跟踪

提出一种新的冠状动脉血管二维运动分析方法,采用活动轮廓(snake)模型技术,对X射线冠状动脉造影图像序列中的血管段进行运动跟踪。把前一帧中snake的停留位置作为当前帧snake的初始位置,通过snake变形得到当前时刻的血管中心线。通过在snake能量函数中嵌入灰度相似度匹配,保证了跟踪结果的准确性。采用临床采集的冠脉造影图像序列对算法进行了验证。