Random walks for image segmentation

A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs     

Iterated random walks with shape prior

We propose a new framework for image segmentation using random walks where a distance shape prior is combined with a region term. The shape prior is weighted by a confidence map to reduce the influence of the prior in high gradient areas and the region term is computed with k-means to estimate the parametric probability density function. Then, random walks is performed iteratively aligning the prior with the current segmentation in every iteration. We tested the proposed approach with natural and medical images and compared it with the latest techniques with random walks and shape priors. The experiments suggest that this method gives promising results for medical and natural images.     

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。     

Semiautomatic segmentation with compact shape prior

In recent years, interactive methods for segmentation are increasing in popularity due to their success in different domains such as medical image processing, photo editing, etc. We present an interactive segmentation algorithm that can segment an object of interest from its background with minimum guidance from the user, who just has to select a single seed pixel inside the object of interest. Due to minimal requirements from the user, we call our algorithm semiautomatic. To obtain a reliable and robust segmentation with such low user guidance, we have to make several assumptions. Our main assumption is that the object to be segmented is of compact shape, or can be approximated by several connected roughly collinear compact pieces. We base our work on the powerful graph cut segmentation algorithm of Boykov and Jolly, which allows straightforward incorporation of the compact shape constraint. In order to make the graph cut approach suitable for our semiautomatic framework, we address several well-known issues of graph cut segmentation technique. In particular, we counteract the bias towards shorter segmentation boundaries and develop a method for automatic selection of parameters. We demonstrate the effectiveness of our approach on the challenging industrial application of transistor gate segmentation in images of integrated chips. Our approach produces highly accurate results in real-time.     

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.     

结合形状约束的Graph Cut行人分割

传统的Graph Cut算法没有对目标的形状予以限制,很难得到语义化的分割结果,即无法保证分割出来的是“行人”。针对该问题提出一种结合形状和底层特征的Graph Cut算法。对于行人分割,用大量真实行人轮廓来表达“行人”的先验形状,对Graph Cut分割算法予以约束,同时构建一个行人模板的层次树以减少匹配时间;并且提出一种区分性的外观模型来替换原来的外观模型。实验结果证明,该算法的分割结果明显优于传统Graph Cut算法的分割结果,所得到的轮廓与真实的行人轮廓比较吻合。     

结合先验形状和Mumford-Shah模型的活动轮廓分割

基于先验形状和Mumford-Shah模型的活动轮廓分割是一种抗噪声干扰、稳定的图像分割方法。该模型采用水平集方法,并结合活动轮廓模型、先验形状和Mumford-Shah模型来控制曲线演化。特定目标的先验知识可以有效地指导目标准确分割,经过主成分分析（PCA）法可以得到感兴趣对象形状的主要信息。通过对不同图片分割实验表明,针对特定的形状,该方法对杂乱背景、部分遮挡、缺失和强噪声的图片依然能得到满意的结果。     

Watershed segmentation using prior shape and appearance knowledge

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.     

甜瓜图像分割的先验LCV模型

以甜瓜形态的微变测量为目标,提出了基于先验形状的LCV(LocalChan-Vese)模型算法。利用形态学方法获得甜瓜的局部信息图像,仿照传统CV模型的拟合形式,建立了LCV能量模型;利用形态学方法及spine插值算法获取甜瓜的先验形状,将其通过形状比较函数嵌入到LCV模型的能量函数中,形成了新的基于先验形状的LCV模型。相比传统边缘检测和图像分割算法,该算法更易于提取出理想的边缘信息。     

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.     

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.     

Adaptive shape prior for recognition and variational segmentation of degraded historical characters

We propose a variational method for model based segmentation of gray-scale images of highly degraded historical documents. Given a training set of characters (of a certain letter), we construct a small set of shape models that cover most of the training set's shape variance. For each gray-scale image of a respective degraded character, we construct a custom made shape prior using those fragments of the shape models that best fit the character's boundary. Therefore, we are not limited to any particular shape in the shape model set. In addition, we demonstrate the application of our shape prior to degraded character recognition. Experiments show that our method achieves very accurate results both in segmentation of highly degraded characters and both in recognition. When compared with manual segmentation, the average distance between the boundaries of respective segmented characters was 0.8 pixels (the average size of the characters was 70*70 pixels).     

Global structure constrained local shape prior estimation for medical image segmentation

Organ shape plays an important role in clinical diagnosis, surgical planning and treatment evaluation. Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction. In most existing works, shape modeling is completed in the original shape space, with the presence of outliers. In addition, the specificity of the patient was not taken into account. This paper proposes a novel target-oriented shape prior model to deal with these two problems in a unified framework. The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques. With this approach, shapes in the training set can be selected according to their intrinsic similarity to the target image. With more accurate shape guidance, an optimized search is performed by a deformable model to minimize an energy functional for image segmentation, which is efficiently achieved by using dynamic programming. Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans. Compared to other existing methods, our proposed method exhibits better performance in both studies.     

Optimal segmentation of pupillometric images for estimating pupil shape parameters

The problem of determining the pupil morphological parameters from pupillometric data is considered. These characteristics are of great interest for non-invasive early diagnosis of the central nervous system response to environmental stimuli of different nature, in subjects suffering some typical diseases such as diabetes, Alzheimer disease, schizophrenia, drug and alcohol addiction. Pupil geometrical features such as diameter, area, centroid coordinates, are estimated by a procedure based on an image segmentation algorithm. It exploits the level set formulation of the variational problem related to the segmentation. A discrete set up of this problem that admits a unique optimal solution is proposed: an arbitrary initial curve is evolved towards the optimal segmentation boundary by a difference equation; therefore no numerical approximation schemes are needed, as required in the equivalent continuum formulation usually adopted in the relevant literature.     

Optimal segmentation of pupillometric images for estimating pupil shape parameters

The problem of determining the pupil morphological parameters from pupillometric data is considered. These characteristics are of great interest for non-invasive early diagnosis of the central nervous system response to environmental stimuli of different nature, in subjects suffering some typical diseases such as diabetes, Alzheimer disease, schizophrenia, drug and alcohol addiction. Pupil geometrical features such as diameter, area, centroid coordinates, are estimated by a procedure based on an image segmentation algorithm. It exploits the level set formulation of the variational problem related to the segmentation. A discrete set up of this problem that admits a unique optimal solution is proposed: an arbitrary initial curve is evolved towards the optimal segmentation boundary by a difference equation; therefore no numerical approximation schemes are needed, as required in the equivalent continuum formulation usually adopted in the relevant literature.     

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。     

基于RandomWalk算法的CT图像肺实质自动分割

针对复杂情况下肺实质的分割问题,提出了一种基于Random Walk算法对肺实质自动分割的方法。首先,根据胸部组织解剖学及其计算机断层扫描(CT)图像的影像学特征,在肺实质及其周围组织分别确定目标区域种子点和背景种子点位置;然后,使用Random Walk算法对CT图像进行分割,提取近似肺区域的掩模;接下来,对掩模实施数学形态学运算,来进一步调整目标区域种子点和背景种子点的标定位置,使其适合具体的复杂情况;最后,再次使用Random Walk算法分割图像,得到最终的肺实质分割结果。实验结果显示,该方法与金标准的平均绝对距离为0.44±0.13 mm,重合率(DC)为99.21%±0.38%。与其他分割方法相比,该方法在分割精度上得到了显著提高。结果表明,提出的方法能够解决复杂情况下肺实质分割的问题,确保了分割的完整性、准确性、实时性和鲁棒性,分割结果和时间均可满足临床需求。     

Multi-view face segmentation using fusion of statistical shape and appearance models

This paper demonstrates how a weighted fusion of multiple Active Shape (ASM) or Active Appearance (AAM) models can be utilized to perform multi-view facial segmentation with only a limited number of views available for training the models. The idea is to construct models only from frontal and profile views and subsequently fuse these models with adequate weights to segment any facial view. This reduces the problem of multi-view facial segmentation to that of weight estimation, the algorithm for which is proposed as well. The evaluation is performed on a set of 280 landmarked static face images corresponding to seven different rotation angles and on several video sequences of the AV@CAR database. The evaluation demonstrates that the estimation of the weights does not have to be very accurate in the case of ASM, while in the case of AAM the influence of correct weight estimation is more critical. The segmentation with the proposed weight estimation method produced accurate segmentations in 91% of 280 testing images with the median point-to-point error varying from two to eight pixels (1.8–7.2% of average inter-eye distance).