多先验形状的图割分割方法*

为了分割图像中的多个目标,提出多先验形状约束的多目标图割分割方法。首先,使用离散水平集框架的形状距离定义先验形状模型,并将这一模型合并到图割框架的区域项中,同时通过加入多类形状先验扩展形状先验能量。然后,通过自适应调节形状先验项的权重系数,实现自适应控制形状项在能量函数中所占的比重,克服人工选择参数的困难,提高分割效率。最后,为使方法对于形状仿射变换具有不变性,使用尺度不变特征变换和随机抽样一致结合的方法进行对准。实验表明,文中方法能够较好分割图像中的多个目标,且能较好克服图像的噪声污染、目标被遮挡等信息缺失问题。     

基于形状先验和梯度约束的彩色图像分割

针对图像遮挡、噪声等复杂场景下,仅依赖颜色信息难以准确分割的问题,将形状先验和图像梯度分别引入马尔科夫随机场框架中,提出一种基于形状先验和梯度约束的彩色图像分割方法。该方法基于颜色特征和形状模版定义能量函数,梯度信息的引入允许待分割目标与形状模版间有一定差异,且待分割目标与形状模版间的变换具有仿射不变性,整个能量函数通过图割算法实现能量最小化,得到最终分割结果。实验结果表明,该方法具有有效性。     

图割与非线性统计形状先验的图像分割

提出一种图割与非线性统计形状先验的图像分割方法。首先,在输入空间对输入的形状模板进行配准,得到训练集;其次,采用非线性核函数将目标形状先验映射到特征空间进行主成分分析,获取其投影形状,将此投影形状映射回原输入空间得到目标的平均形状,构成新的能量函数;第三,通过自适应调整形状先验项的权值系数,使能量函数的形状先验项自适应于被分割的图像;最后,用Graph Cuts方法最小化能量函数完成图像分割。实验结果表明,该方法不仅能准确分割与形状先验模板有差别的图像,而且对目标有遮挡或污染的图像也有较好的分割效果,提高了分割效率。     

融合Kernel PCA形状先验信息的变分图像分割模型

目的 基于能量最小化的变分图像分割方法已经受到研究人员的广泛重视,取得了丰硕成果。但是,针对图像中存在的噪音污染、目标被遮挡等情况,则难以正确分割。引入先验形状信息是解决该问题的一个重要方向,但是随之而带来的姿态变化问题是一个难点。传统的做法是在每步迭代过程中单独计算姿态变换参数,导致计算量大。方法 在基于Kernel PCA(KPCA)的形状先验模型基础上,提出一种具有内在的姿态不变性的KPCA形状先验模型,并将之融合到C-V变分图像分割模型中。结果 提出模型无须在每步迭代中显式地单独计算姿态变换参数,相对于C-V模型分割正确率能够提高7.47%。同时,针对KPCA模型中计算高斯核函数的参数σ取值问题,也给出一种自适应的计算方法。结论 理论分析及实验表明该模型能较好地解决先验形状与目标间存在的仿射变化问题,以及噪音、目标被遮挡等问题。     

先验形状力场参数活动轮廓模型及其医学图像分割

先验形状参数活动轮廓模型是一种抗噪声干扰稳定的图像分割方法.它具有对弱边缘、凹区域进行分割的能力,同时有较大的边缘捕捉范围.通过引入一种非距离性的先验形状力场,构建一种新的能反映先验形状的参数活动轮廓模型.新的先验形状活动轮廓模型避免了曲线之间距离的计算,减少了模型的复杂性.新的方法可以较好地解决传统型参数活动轮廓模型的一些本质缺陷.实验对带噪声且为弱边缘的医学CT图像和超声图像进行分割能得到理想的边缘轮廓.     

稀疏形状先验的脑肿瘤图像分割

目的 在脑部肿瘤图像的分析过程中,准确分割出肿瘤区域对于计算机辅助脑部肿瘤疾病的诊断及治疗过程具有重要意义。然而,由于脑部图像常存在结构复杂、边界模糊、灰度不均以及肿瘤内部存在明暗区域的问题,使得肿瘤图像分割工作面临严峻挑战。为了克服上述困难,更好地实现脑部肿瘤图像分割,提出一种基于稀疏形状先验的脑肿瘤图像分割算法。方法 首先,研究脑部肿瘤图像的配准与形状描述,并以此为基础构建脑部肿瘤的稀疏形状先验约束模型;继而,将该稀疏形状先验约束模型与区域能量描述方法相结合,构建基于稀疏形状先验的能量函数;最后,对能量函数进行优化及迭代,输出脑部肿瘤区域分割结果。结果 本文使用脑胶质瘤公开数据集BraTS2017进行算法测试,本文算法的分割结果与真实数据之间的平均相似度达到93.97%,灵敏度达到91.3%,阳性预测率达到95.9%。本文算法的实验准确度较高,误判率较低,鲁棒性较强。结论 本文算法能够结合水平集方法在拓扑结构描述和稀疏表达方法在复杂形状表达方面的优势,同时由于加入了形状约束,能够有效削弱肿瘤内部明暗区域对分割结果造成的影响,从而更准确和稳定地实现脑部肿瘤图像分割。     

基于先验形状信息的水平集图像分割

针对现有水平集方法对于具有强噪声或弱边界的目标进行分割时存在的问题,提出了一种基于形状先验的图像分割方法.该模型采用变分水平集方法,融合了区域特征和边界轮廓特征,并通过相似性匹配选择最佳先验形状.该模型不仅对具有强噪声和弱边界的复杂图像具有较好的分割效果,而且有效地解决了曲线演化的初始轮廓的确定问题.与传统方法进行对比实验,结果表明,该方法具有较好的分割效果和较高的准确率.     

基于形状先验和图割的彩色图像分割

基于图割理论的图像分割方法在二值标号问题中可以获取全局最优解,而在多标号问题中可以获取带有很强特征的局部最优解。但对于含有噪声或遮挡物等复杂的图像,分割结果不完整,效果并不令人满意,提出了一种基于形状先验和图割的图像分割方法。以图割算法为基础,加入形状先验知识,使该算法包含更多约束信息,从而限制感兴趣区域的搜寻空间,能够更好地分割出完整的目标,增加了算法的精确度。针对形状的仿射变换,运用特征匹配算法进行处理,使算法更加具有灵活性,能够应对不同类型的情况。实验表明了该算法的有效性。     

融合形状先验的向量CV模型图像分割算法

Chan等人提出的向量CV模型尽管解决了传统CV模型无法分割向量值图像的问题,但是向量CV模型对于含有噪声或遮挡物等复杂的图像,无法正确分割目标。针对此问题提出一种融合形状先验向量CV模型。其能量泛函主要包含形状先验项、图像区域信息项以及距离正则项。此能量函数使得主动轮廓和形状先验位置相近时停止演化。该模型所用形状模板可以与目标形状仿射不同,使得算法更加灵活。该模型对含噪以及目标遮挡的图像具有很好的分割效果。     

先验形状约束的超声前列腺图像分割方法

提出一种结合超声前列腺图像的局部特征和前列腺的先验形状知识的分割方法。该方法在传统图像分割方法中引入了前列腺的先验形状约束,使得分割能够一定程度地避免由于超声图像中噪声、伪影、灰度分布不均匀等因素对前列腺分割所造成的影响。算法分为两个部分：先验形状模型的学习和先验形状约束的分割。在先验形状模型学习阶段,采用主成分分析方法对形状作特征提取,以高斯分布作为形变参数的估计;在先验形状约束分剖阶段,将基于局部高斯拟合特征的活动轮廓模型与形状模型相结合对前列腺图像分割。实验表明,所提出的方法在超声前列腺图像中取得了良好的分割效果,为临床诊断和治疗提供了定量分析的工具。     

Image segmentation with one shape prior — A template-based formulation

Image segmentation with one shape prior is an important problem in computer vision. Most algorithms not only share a similar energy definition, but also follow a similar optimization strategy. Therefore, they all suffer from the same drawbacks in practice such as slow convergence and difficult-to-tune parameters. In this paper, by reformulating the energy cost function, we establish an important connection between shape-prior based image segmentation with intensity-based image registration. This connection enables us to combine advanced shape and intensity modeling techniques from segmentation society with efficient optimization techniques from registration society. Compared with the traditional regularization-based approach, our framework is more systematic and more efficient, able to converge in a matter of seconds. We also show that user interaction (such as strokes and bounding boxes) can easily be incorporated into our algorithm if desired. Through challenging image segmentation experiments, we demonstrate the improved performance of our algorithm compared to other proposed approaches.     

Individual tooth segmentation from CT images using level set method with shape and intensity prior

3D visualization of teeth from CT images provides important assistance for dentists performing orthodontic surgery and treatment. However, dental CT images present several major challenges for the segmentation of tooth, which touches with adjacent teeth as well as surrounding periodontium and jaw bones. Moreover, tooth contour suffers from topological changes and splits into several branches. In this work, we focus on the segmentation of individual teeth with complete crown and root parts. To this end, we propose adaptive active contour tracking algorithms: single level set method tracking for root segmentation to handle the complex image conditions as well as the root branching problem, and coupled level set method tracking for crown segmentation in order to separate the touching teeth and create the virtual common boundaries between them. Furthermore, we improve the variational level set method in several aspects: gradient direction is introduced into the level set framework to prevent catching the surrounding object boundaries; in addition to the shape prior, intensity prior is introduced to provide adaptive shrinking or expanding forces in order to deal with the topological changes. The test results for both tooth segmentation and 3D reconstruction show that the proposed method can visualize individual teeth with high accuracy and efficiency.     

Graph cut segmentation with a statistical shape model in cardiac MRI

Segmenting the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. The segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a segmentation method based on a statistical shape model obtained with a principal component analysis (PCA) on a set of representative shapes of the RV. Shapes are not represented by a set of points, but by distance maps to their contour, relaxing the need for a costly landmark detection and matching process. A shape model is thus obtained by computing a PCA on the shape variations. This prior is registered onto the image via a very simple user interaction and then incorporated into the well-known graph cut framework in order to guide the segmentation. Our semi-automatic segmentation method has been applied on 248 MR images of a publicly available dataset (from MICCAI’12 Right Ventricle Segmentation Challenge). We show that encouraging results can be obtained for this challenging application.     

多尺度分割先验迭代重加权低秩恢复显著性检测

目的 现有显著性检测方法大多只关注显著目标的中心信息,使得算法只能得到中心清晰、边缘模糊的显著目标,丢失了一些重要的边界信息,而使用核范数约束进行低秩矩阵恢复,运算过程冗余。为解决以上问题,本文提出一种无监督迭代重加权最小二乘低秩恢复算法,用于图像视觉显著性检测。方法 将图像分为细中粗3种尺度的分割,从细粒度和粗粒度先验的融合中得到分割先验信息;将融合后的分割先验信息通过迭代重加权最小二乘法求解平滑低秩矩阵恢复,生成粗略显著图;使用中粒度分割先验对粗略显著图进行平滑,生成最终的视觉显著图。结果 实验在MSRA10K（Microsoft Research Asia 10K）、SOD（salient object detection dataset）和ECSSD（extended complex scene saliency dataset）数据集上进行测试,并与现有的11种算法进行对比。结果表明,本文算法可生成边界清晰的显著图。在MSRA10K数据集上,本文算法实现了最高的AUC（area under ROC（receiver operating characteristic）curve）和F-measure值,MAE（mean absolute error）值仅次于SMD（structured matrix decomposition）算法和RBD（robust back ground detection）算法,AUC和F-measure值比次优算法RPCA（robust principal component analysis）分别提高了3.9%和12.3%;在SOD数据集上,综合AUC、F-measure和MAE值来看,本文算法优于除SMD算法以外的其他算法,AUC值仅次于SMD算法、SC（smoothness constraint）算法和GBVS（graph-based visual salieney）算法,F-measure值低于最优算法SMD 2.6%;在ECSSD数据集上,本文算法实现了最高的F-measure值75.5%,AUC值略低于最优算法SC 1%,MAE值略低于最优算法HCNs（hierarchical co-salient object detection via color names）2%。结论 实验结果表明,本文算法能从前景复杂或背景复杂的显著图像中更准确地检测出边界清晰的显著目标。     

基于先验形状的混杂活动轮廓模型及其在图像分割中的应用

提出了一种新颖的基于先验形状学习的混杂活动轮廓(SHAC)模型,该模型采用变分水平集方法,融合自适应区域信息与边界信息,运用主成分分析的方法从给定的含有目标物体轮廓的训练集学习得到最佳形状信息,并将其作为先验形状。将自适应区域特征和轮廓特征作为局部信息,先验形状作为全局信息,在迭代过程中结合全局和局部信息实现对演化曲线的形变进行指导和约束,达到分割目标物体的目的。通过定量和定性地分析低对比度的乳腺核磁共振图像中的乳腺轮廓的分割,以及具有复杂背景的自然图像中感兴趣区域的分割结果,验证了SHAC模型比传统活动轮廓模型具有更高的准确率,表明了该模型不仅提高了图像分割中对弱边界的识别度,减弱了非目标轮廓的干扰,而且具有良好的抗噪能力。     

A shape prior constraint for implicit active contours

We present a shape prior constraint to guide the evolution of implicit active contours. Our method includes three core techniques. Firstly, a rigid registration is introduced, using a line search method within a level set framework. The method automatically finds the time step for the iterative optimization processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances the impact of the shape prior versus the image guidance of the active contour. A mixed stopping condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our method is completely non-parametric and avoids taking linear combinations of non-linear signed distance functions, which would cause problems because distance functions are not closed under linear operations. Experimental results show that our method is able to extract the desired objects in several circumstances, namely when noise is present in the image, when the objects are in slightly different poses and when parts of the object are invisible in the image.     

基于多尺度统计形状模型的Levelset分割方法

提出并建立了一种基于小波分析的多尺度统计模型,将该统计模型作为先验知识引入Mumford-Shah能量约束函数,从而指导水平集函数进行图像分割。实验表明,当对拓扑结构复杂的医学图像进行分割时,该方法具有明显的效果,同时分割速度和精度都得到了明显改善。     

Parametric estimation of affine deformations of planar shapes

We consider the estimation of affine transformations aligning a known 2D shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the shapes and then compute the transformation parameters from these landmarks. Here we propose a novel approach where the exact transformation is obtained as the solution of a polynomial system of equations. The method has been tested on synthetic as well as on real images and its robustness in the presence of segmentation errors and additive geometric noise has also been demonstrated. We have successfully applied the method for the registration of hip prosthesis X-ray images. The advantage of the proposed solution is that it is fast, easy to implement, has linear time complexity, works without established correspondences and provides an exact solution regardless of the magnitude of transformation.     

融合分割先验的多图像目标语义分割

目的 在序列图像或多视角图像的目标分割中,传统的协同分割算法对复杂的多图像分割鲁棒性不强,而现有的深度学习算法在前景和背景存在较大歧义时容易导致目标分割错误和分割不一致。为此,提出一种基于深度特征的融合分割先验的多图像分割算法。方法 首先,为了使模型更好地学习复杂场景下多视角图像的细节特征,通过融合浅层网络高分辨率的细节特征来改进PSPNet-50网络模型,减小随着网络的加深导致空间信息的丢失对分割边缘细节的影响。然后通过交互分割算法获取一至两幅图像的分割先验,将少量分割先验融合到新的模型中,通过网络的再学习来解决前景/背景的分割歧义以及多图像的分割一致性。最后通过构建全连接条件随机场模型,将深度卷积神经网络的识别能力和全连接条件随机场优化的定位精度耦合在一起,更好地处理边界定位问题。结果 本文采用公共数据集的多图像集进行了分割测试。实验结果表明本文算法不但可以更好地分割出经过大量数据预训练过的目标类,而且对于没有预训练过的目标类,也能有效避免歧义的区域分割。本文算法不论是对前景与背景区别明显的较简单图像集,还是对前景与背景颜色相似的较复杂图像集,平均像素准确度（PA）和交并比（IOU）均大于95%。结论 本文算法对各种场景的多图像分割都具有较强的鲁棒性,同时通过融入少量先验,使模型更有效地区分目标与背景,获得了分割目标的一致性。     

Incorporating shape prior into geodesic active contours for detecting partially occluded object

A new method to incorporate shape prior knowledge into geodesic active contours for detecting partially occluded object is proposed in this paper. The level set functions of the collected shapes are used as training data. They are projected onto a low dimensional subspace using PCA and their distribution is approximated by a Gaussian function. A shape prior model is constructed and is incorporated into the geodesic active contour formulation to constrain the contour evolution process. To balance the strength between the image gradient force and the shape prior force, a weighting factor is introduced to adaptively guide the evolving curve to move under both forces. The curve converges with due consideration of both local shape variations and global shape consistency. Experimental results demonstrate that the proposed method makes object detection robust against partial occlusions.